{"meta":{"version":"1.0","lastUpdated":"2026-05","purpose":"Structured professional profile data — same source of truth as the rendered site","license":"Free to use for ATS, recruiting tools, custom assistants, and research. No attribution required."},"identity":{"name":"Michael Novack","alternateNames":["Mike Novack","Michael J. Novack"],"currentRole":"Director of Product, Lyft Business","currentTitle":"Director of Product, Lyft Business","lane":"AI product operator — senior IC and product leadership at frontier AI companies and AI-native consumer or developer products","titles":["Director of Product","Senior Director of Product","Group Product Lead"],"tagline":"Turning ambiguous problems into products that scale, from zero to billions","philosophy":"In probabilistic systems, the eval set is the product spec. The PM's core lever is the objective function, the curated evaluation dataset, and the error tolerances the deployment gate allows — not a deterministic PRD. From there: navigate the latency-accuracy-cost frontier per task, design UX for low-confidence outputs, and earn user trust when the model is wrong."},"contact":{"email":"hi@builtbymikey.com","website":"https://builtbymikey.com","linkedin":"https://www.linkedin.com/in/michaeljnovack/","github":"https://github.com/michaelnnovack","twitter":"@michaeljnovack","schedulingLink":"https://www.builtbymikey.com/schedule"},"location":{"primary":{"city":"Toronto","region":"Ontario","country":"Canada"},"secondary":{"city":"San Francisco","region":"California","country":"United States"},"availability":{"remote":true,"usMarkets":true,"relocationInterest":["San Francisco","Silicon Valley","Bay Area"]}},"expertiseAreas":[{"title":"Product Strategy","description":"Turning user problems into product decisions that actually matter","details":"Strategy starts with what users actually struggle with, not what we assume they need. The hard part is saying no to good ideas so the team can execute the great ones.","skills":["Strategic Prioritization","User Research","Roadmapping"]},{"title":"AI Product Development","description":"The eval set is the product spec","details":"In probabilistic systems you can't write a deterministic spec, so the eval set defines what the model must get right and which failures you refuse to ship. Trust is the product, which means users should understand what happened and fix it cheaply when it misses.","skills":["Eval Set as Spec","Objective Function Design","Latency–Accuracy–Cost"]},{"title":"Growth & Scaling","description":"Building repeatable systems that compound over time","details":"Growth isn't a hack. It's finding the moment users get value and removing every obstacle in front of it. Durable growth comes from products people genuinely want to use, not clever tricks.","skills":["Experimentation Design","Metric Definition","Growth Analytics"]},{"title":"Team Leadership","description":"Getting out of the way so teams can do their best work","details":"Set direction, not tactics. Make the problem clear, explain why it matters, then let the team find the how. Trust is built by surfacing problems early and planning the recovery, not by projecting false confidence.","skills":["Context Setting","Failure-Mode Design","Performance Coaching"]},{"title":"Business Operations","description":"Removing the friction that slows teams down","details":"Most operational problems are communication problems: too many meetings, unclear ownership, decisions that need five approvals. I find where work gets stuck and simplify until the path is obvious, because the best process is the one nobody notices.","skills":["Process Optimization","System Design","Automation Strategy"]},{"title":"Strategic Finance","description":"Using numbers to make better decisions, faster","details":"Finance isn't spreadsheets. It's understanding tradeoffs: this feature or that market, the real cost of moving faster. I build models that answer those questions clearly, so teams can decide with confidence. Clarity beats precision.","skills":["Financial Modeling","Capital Allocation","Strategic Planning"]}],"technicalDomains":[{"domain":"AI/ML Product Development","capabilities":["Generative AI and LLM product development (Anthropic Claude, prompt engineering, structured output)","Machine learning classification models","Recommendation engines and personalization systems","Computer vision systems","Autonomous systems (Level 4 autonomy)","ML model product requirements and evaluation","AI agent architecture and tool-calling design","Retrieval-augmented generation (RAG) and grounded LLM responses"],"examples":["Tuned the F-beta target on intake classification at Hims and built the offline regression eval + online deployment gate that lifted conversion 18% without regressing safety signals","Defined the recommendation product spec and activation surfaces at Dropbox ($8M expansion revenue)","Specced TrunkVision's product requirements and operator handoff UX at Agtonomy (99.7% mission success)","Owned the Level 4 sidewalk autonomy product spec at Serve Robotics (Postmates) — regression eval sets, safety-critical thresholding, depot-release deployment gate, and operator-intervention UX","Built Field Kit — a suite of custom AI product management tools using the Anthropic Claude API (Sonnet 4.6 + Haiku 4.5) via the Vercel AI SDK, with streaming structured output and Zod-validated JSON schemas. Demonstrates hands-on AI product leadership: prompt engineering, model routing for cost/latency tradeoffs, and retrieval-augmented grounding. Live at builtbymikey.com/field-kit","Built Ask Enzo — a conversational AI agent on builtbymikey.com backed by Claude Haiku 4.5 with seven server-side tool-calling functions for portfolio querying"]},{"domain":"Marketplace Architecture","capabilities":["Two-sided and three-sided marketplace design","Supply-demand balancing and matching algorithms","Platform economics and pricing mechanisms","Multi-country marketplace scaling"],"examples":["Transformed Just Eat Takeaway from 2-sided to 3-sided marketplace (0 to 1B+ deliveries)","Scaled Just Eat Takeaway marketplace across 14 countries with 100K+ active couriers","Built autonomous delivery marketplace at Serve Robotics (Postmates)"]},{"domain":"Payment and Fraud Systems","capabilities":["Payment orchestration and optimization","Fraud detection systems","Dynamic pricing infrastructure","Conversion rate optimization"],"examples":["Built dual-path payment system at Hims reducing fraud 23% + improving conversion 8%","Implemented dynamic pricing at Hims improving LTV 31%","Optimized payment flows generating $100M+ in GLP-1 revenue"]},{"domain":"Autonomous Systems","capabilities":["Level 4 autonomy product strategy","Fleet management platforms","Operator tool design and UX","Real-world deployment and operations"],"examples":["Designed TeleFarmer platform at Agtonomy achieving 1:10 operator-to-machine ratios","Built fleet management at Serve Robotics (Postmates) enabling 1:8 ratios","Completed 1,000+ autonomous deliveries across 5 markets at Serve Robotics"]},{"domain":"Product-Led Growth","capabilities":["Self-serve onboarding design","Conversion funnel optimization","Behavioral analytics and segmentation","Revenue attribution systems"],"examples":["Led PLG transformation at PwC, reducing CAC 40% and eliminating $50M+ waste","Built multi-product activation at Dropbox (35% adoption increase)","Created personalized onboarding at Hims (18% conversion lift)"]}],"projects":[{"id":"lyft-business-travel","title":"Lyft: Business Travel as a Competitive Advantage","subtitle":"Accelerating business travel via product-led growth","company":"Lyft","role":"Director of Product","problem":"Business travel ground transportation is fragmented and expensive for companies to manage. Employees default to personal ride choices, finance teams lack visibility into spend, and existing enterprise solutions create friction that drives low adoption.","approach":"Leading product strategy for Lyft Business, focused on expanding enterprise adoption and improving the core business travel experience through product-led growth motions.","outcome":"Currently in progress. Focused on building the product foundations that will scale Lyft's business travel platform.","metrics":["Leading product strategy for Lyft Business travel platform","Driving enterprise adoption through product-led growth"],"technologies":["Product-Led Growth","B2B Platform","Travel Optimization","Enterprise Solutions"],"website":"https://www.lyft.com/business","keyTakeaways":[]},{"id":"hims-glp1-platform","title":"Hims & Hers: GLP-1 Launch & Platform Modernization","subtitle":"Launching weight loss GLP-1s while rebuilding payment and personalization systems","company":"Hims & Hers","role":"Senior Director of Product","problem":"Hims & Hers was growing quickly, initially focused on hair loss and erectile dysfunction medication, but was starting to lean into GLP-1s. Our platform wasn't built to support this yet. The demand was high, but we faced serious challenges:\n\n• Fraud was skyrocketing as treatment costs jumped from $30 to $1,500, threatening our ability to serve legitimate customers at scale\n\n• Payment conversion was tanking because our fraud rules were too aggressive, blocking real customers who wanted treatment\n\n• We couldn't personalize experiences, so every customer got the same generic flow regardless of their needs or likelihood to convert\n\n• Every pricing experiment required custom engineering, making it impossible to move fast and find the right price points for different customer segments","approach":"I led three platform initiatives that ran in parallel:\n\n• Payment funnel optimization: Split the payment flow into two separate paths. One optimized for fraud detection on high-risk transactions, another for conversion on everything else. This let us catch fraudsters without punishing legitimate customers.\n\n• ML-powered personalization: Partnered with DS/ML to ship intake-time intent classification. Owned the label rubric (high-intent vs. exploratory) and tuned the F-beta target so the funnel weighted precision higher than recall — false positives push exploratory users into a fast-checkout flow for a regulated medication, so the cost asymmetry is real and quantifiable. Designed the cold-start fallback for users with too little signal to classify, and built the offline regression eval set + online deployment gate that blocked any model iteration that regressed conversion, LTV, or downstream clinical-appropriateness signals. The UX translated model confidence into shorter or longer onboarding — high-confidence users went straight to checkout, low-confidence users saw more educational content to build trust.\n\n• Dynamic pricing infrastructure: Launched a CDP-powered incentives system that automatically served targeted discounts based on customer data. This unlocked rapid pricing experiments across different segments without needing engineering for each test.","outcome":"The platform transformation enabled Hims & Hers to successfully enter the GLP-1 market while building infrastructure that would support the company for years:\n\n• **Market entry:** The GLP-1 launch generated over $100M in incremental revenue in its first year, establishing Hims & Hers as a legitimate player in the weight loss medication space\n\n• **Payment economics:** We solved the fraud-vs-conversion tradeoff by reducing fraud 23% while simultaneously improving conversion 8%. The key insight was that you don't need to sacrifice one for the other with the right architecture\n\n• **Personalization:** Personalized onboarding lifted conversion 18% and lowered acquisition costs, but more importantly, it made the experience feel tailored rather than generic\n\n• **Pricing velocity:** Dynamic pricing drove 31% improvement in lifetime value. We could test new pricing strategies in days instead of months, running experiments 40% faster than before\n\n• **Platform foundation:** These weren't just one-time wins. The platform capabilities we built became the foundation for every future product launch at Hims & Hers","metrics":["$100M+ incremental revenue from GLP-1 platform","23% reduction in fraud while improving payment conversion 8%","18% lift in onboarding conversion via ML personalization","31% improvement in LTV through dynamic pricing experiments","40% faster experiment velocity via incentives platform"],"technologies":["Payment Orchestration","Fraud Detection","Intent Classification","F-beta Tuning","Regression Eval Sets","Deployment Gating","Cold-Start UX","CDP Integration","Dynamic Pricing","A/B Testing"],"website":"https://www.hims.com","keyTakeaways":["Platform thinking: built infrastructure enabling future products","Balancing competing constraints: reduced fraud while improving conversion","ML personalization at scale: 18% conversion lift","Dynamic pricing velocity: 40% faster experiments"]},{"id":"dropbox-multi-product-adoption","title":"Dropbox: The Next Phase of Growth via Multi-Product Adoption","subtitle":"Transforming single-product users into multi-product power users","company":"Dropbox","role":"Director of Product & Strategy","problem":"Dropbox had become synonymous with cloud storage, but that perception was actually limiting growth. The company had acquired and built amazing products like DocSend, HelloSign, and Capture, yet users weren't discovering them. We faced critical challenges:\n\n• 90% of users only touched core storage, completely missing the adjacent products that could make their workflows more powerful\n\n• Our acquisition strategy had created siloed products with separate onboarding flows, so users never saw the full ecosystem\n\n• Retention was plateauing because single-product users churned at much higher rates than multi-product users\n\n• We had no way to intelligently recommend the right product at the right time based on user behavior and needs","approach":"I led a cross-functional initiative to transform how users discovered and adopted Dropbox products:\n\n• Smart onboarding redesign: Rebuilt the first-time user experience to intelligently showcase relevant products based on signup context. Sales professionals saw DocSend, while creative teams saw Capture and Replay.\n\n• Behavioral recommendation engine: Built a \"right product, right user, right time\" ML system that analyzed storage behaviors to surface product recommendations. For example, users frequently sharing large presentations got nudged toward DocSend for better analytics and control.\n\n• Seamless activation flows: Created lightweight in-app experiences that let users try adjacent products without leaving their current workflow. Someone could activate HelloSign to collect a signature without ever leaving a shared folder.","outcome":"We successfully repositioned Dropbox from a storage utility to a complete content collaboration platform, unlocking a new phase of sustainable growth:\n\n• **Revenue expansion:** Generated $8M+ in incremental expansion revenue from multi-product adoption, proving the strategy could drive meaningful financial impact\n\n• **Adoption velocity:** Increased users adopting 2+ products by 35%, fundamentally changing how people thought about and used Dropbox\n\n• **Retention:** Multi-product users showed 25% better 30-day retention, validating that deeper product engagement creates stickier customers\n\n• **Discovery speed:** Users found value in new products 60% faster through intelligent recommendations versus hoping they'd stumble upon features organically\n\n• **Platform scale:** The activation framework we built scaled across 6 different product lines, becoming Dropbox's standard approach for launching and growing new products","metrics":["$8M+ incremental expansion revenue from multi-product users","35% increase in users adopting 2+ products","25% improvement in 30-day retention for multi-product users","60% faster time-to-value for new product discovery","Cross-product activation flows deployed across 6 product lines"],"technologies":["Product Recommendation ML","User Journey Mapping","Cross-Product Analytics","Onboarding Optimization","Activation Funnels"],"website":"https://www.dropbox.com","keyTakeaways":["Multi-product adoption strategy driving expansion revenue","Intelligent recommendation engines increasing discovery","Activation frameworks scaling across product lines","Retention improvement through deeper product engagement"]},{"id":"agtonomy-autonomous-fleet-platform","title":"Agtonomy: Bringing Autonomy to Agriculture","subtitle":"Founding-team product for autonomous farming — TeleFarmer operator UX and TrunkVision CV product spec","company":"Agtonomy","role":"Founding Team Member","problem":"Specialty crop farming (vineyards, orchards, and citrus groves) was facing a perfect storm. Labor shortages meant farmers couldn't find enough workers during critical harvest windows, and the equipment needs were 3x higher than traditional row crops. We saw key challenges:\n\n• Farmers needed to trust unproven startups with their entire harvest, which wasn't realistic for established operations\n\n• Major equipment manufacturers had the farmer relationships and distribution but lacked the AI expertise to build autonomous systems\n\n• No one had solved the operator experience problem: how do you manage multiple autonomous tractors simultaneously across complex agricultural terrain?\n\n• Existing autonomous solutions required line-of-sight supervision, making the economics unworkable","approach":"Joined as an early founding team member to build the 0-1 product and help shape the go-to-market strategy:\n\n• TeleFarmer operator platform: Designed the mobile-first interface that lets a single operator manage 10+ autonomous machines simultaneously. The key insight was treating it like fleet management, not individual robot control.\n\n• Computer vision for specialty crops: Built TrunkVision, our CV system for centimeter-precision navigation between vine rows and around tree trunks. This was critical because GPS alone isn't accurate enough in these environments.\n\n• OEM partnership strategy: Rather than building tractors from scratch, positioned Agtonomy as the \"AI Factory\" that partners with the \"Iron Factory\" equipment manufacturers. This let us focus on autonomy while leveraging their existing farmer relationships.","outcome":"Created the foundational technology and strategy that established Agtonomy as the autonomous agriculture platform:\n\n• **Operator efficiency:** The TeleFarmer platform achieved 1:10+ operator-to-machine ratios, fundamentally changing the economics of autonomous farming\n\n• **Autonomy performance:** 99.7% mission success rate across thousands of autonomous operations proved the technology was production-ready for real farms\n\n• **OEM validation:** Partnership platform now powers integrations with Bobcat, Kubota, and other major manufacturers, validating the \"AI + Iron\" collaboration model\n\n• **Commercial traction:** Deployed at premium vineyards including E&J Gallo, Treasury Wine Estates, and Silver Oak, showing enterprise customers would trust the technology\n\n• **Company trajectory:** The founding team vision and early product work helped raise $42.8M+ and scale pilot programs 500%, establishing Agtonomy as the category leader","metrics":["1:10+ operator-to-machine management ratio via TeleFarmer platform","99.7% autonomous mission success rate in production deployments","Centimeter-precision navigation in specialty crop environments","Partnership integrations with 5+ major equipment manufacturers","Commercial deployments at E&J Gallo, Treasury Wine Estates, Silver Oak","$42.8M+ funding raised; 500% pilot program expansion from founding work"],"technologies":["Computer Vision (TrunkVision)","Fleet Management Platform","Hybrid Autonomy Systems","Real-time Telemetry","Mobile-first Operator UX","OEM Integration"],"website":"https://www.agtonomy.com","keyTakeaways":["0-1 product development in novel category","Fleet management economics through operator efficiency","OEM partnership strategy for go-to-market","Computer vision and autonomy at production scale"]},{"id":"postmates-serve-robotics","title":"Serve Robotics (Postmates): Creating the Future of Last-Mile Delivery","subtitle":"Owning the Level 4 sidewalk autonomy product spec, fleet platform, and customer/merchant experience","company":"Serve Robotics (Postmates)","role":"Director of Product & Business Operations","problem":"Short-distance deliveries were destroying unit economics and creating unsustainable delivery operations. Postmates faced fundamental challenges:\n\n• Deliveries under 1 mile were unprofitable, losing money on every order because sending a car and driver for a burrito made no economic sense\n\n• Driver churn was hitting 40% as couriers avoided short, low-paying trips, creating constant recruiting costs\n\n• No one had built a complete product experience for autonomous delivery. How would customers receive packages from a robot? How would merchants hand off orders?\n\n• The autonomy stack needed to work in real cities with real chaos, not just controlled environments","approach":"Pioneered the 0-1 development of autonomous sidewalk delivery, building the product and autonomy systems from the ground up:\n\n• Autonomy product spec: Owned the Level 4 sidewalk autonomy product spec — designed the regression eval sets (per-scenario perception, planning under occlusion, novel sidewalk geometries) and the safety-critical thresholding the perception team optimized against. Owned the depot-release deployment gate: no robot left the lot until eval coverage cleared, and the operator-intervention UX caught the long-tail edge cases the model would not handle on its own. The product bar was predictable and safe over fast and clever.\n\n• Operator platform: Designed fleet management tools enabling 1:many operator-to-robot supervision. Built remote intervention systems so one operator could monitor multiple robots across the city, making the unit economics viable.\n\n• Customer and merchant experience: Owned the end-to-end delivery product from merchant handoff to customer unlock. Designed intuitive robot unlock flows, merchant integration workflows, and optimized every touchpoint to make autonomous delivery feel seamless and delightful.\n\n• Delivery optimization: Redesigned the delivery experience through user research, reducing pickup time by 35% and cutting support interventions by 300%, proving the product could scale efficiently.","outcome":"Built the foundational product and technology that established autonomous sidewalk delivery as a viable category:\n\n• **0-1 Launch:** Pioneered first commercial autonomous delivery robots, managing hardware partnerships and regulatory approvals to launch across 5 markets\n\n• **Delivery performance:** 1,000+ autonomous deliveries proving the product and autonomy systems worked in real-world conditions\n\n• **Operational efficiency:** Achieved 1:8 operator-to-robot ratios through smart fleet management, fundamentally changing the economics of last-mile delivery\n\n• **Product experience:** 35% faster pickup times and 300% reduction in support interventions showed customers and merchants could trust robots\n\n• **Company impact:** The product and technology foundation enabled Serve to spin out as an independent company, ultimately going public and partnering with Uber Eats for large-scale deployment","metrics":["Pioneered 0-1 autonomous delivery across 5 markets","1,000+ deliveries managed through product and autonomy systems","1:8 operator-to-robot ratio via fleet management platform","35% reduction in pickup time; 300% fewer support interventions","Platform enabled Serve spinout, IPO, and Uber Eats partnership"],"technologies":["Level 4 Autonomy","Regression Eval Sets","Safety-Critical Thresholding","Depot-Release Deployment Gate","Operator-Intervention UX","Fleet Management Platform","Robot UX Design","User Research"],"website":"https://www.serverobotics.com","keyTakeaways":["Pioneering autonomous delivery category","Operator platform design for fleet management","Real-world autonomy deployment and operations","Customer/merchant experience for novel interaction models"]},{"id":"justeat-3sided-marketplace","title":"Just Eat Takeaway: Building a 3-Sided Delivery Marketplace","subtitle":"Scaling from 0 to 1B+ deliveries through courier network and acquisition integration","company":"Just Eat Takeaway","role":"Group Product Lead","problem":"Just Eat had built a successful 2-sided marketplace connecting restaurants with customers, but we were falling behind. Deliveroo and Uber Eats were offering faster, more reliable delivery with their own courier networks, and we were stuck relying on restaurants to handle their own delivery. We faced existential challenges:\n\n• Restaurant-managed delivery was slow and inconsistent, creating poor customer experiences that drove users to competitors\n\n• We had no control over the delivery experience, the most critical part of the customer journey\n\n• Venture-backed competitors were aggressively expanding with their 3-sided models (customers, restaurants, couriers), threatening our market position\n\n• We needed to add an entire third side to our marketplace (couriers) without breaking the existing business or alienating restaurant partners","approach":"Led the complete transformation from 2-sided to 3-sided marketplace, building courier operations from scratch while integrating a major acquisition:\n\n• Courier marketplace strategy: Formulated the marketplace strategy and platform architecture for our courier network. Built the 0-1 product including courier-facing mobile apps, real-time dispatch systems, and operational frameworks that could scale globally.\n\n• Skip acquisition integration: Executed the £200M Skip acquisition, overseeing technical consolidation and user migration. This wasn't just merging codebases. It was combining two different marketplace models and migrating millions of customers without service disruption.\n\n• Global expansion: Scaled from single-market proof-of-concept to 14-country rollout. Built the platform to handle diverse regulations, cultural differences, payment systems, and operational models. Each market needed localization while maintaining a unified platform.\n\n• Product development: Managed development of web and mobile apps with real-time tracking, dynamic routing, and payment systems. Built the tools restaurants needed to integrate with our courier network seamlessly.","outcome":"Successfully transformed Just Eat into a full-service delivery platform, enabling the company to compete with venture-backed rivals while maintaining profitability:\n\n• **Platform scale:** Scaled from 0 to 1 billion+ deliveries, establishing Just Eat as a dominant player in food delivery across multiple markets\n\n• **Marketplace growth:** Connected 10M+ customers with restaurants through the new 3-sided marketplace, generating £50M+ in annual revenue\n\n• **Acquisition success:** The Skip integration drove 550% YoY order growth, proving we could successfully merge and scale acquired marketplaces\n\n• **Operational excellence:** Achieved 90%+ delivery accuracy across diverse global markets with 20%+ courier retention, showing the operational model worked at scale\n\n• **Network expansion:** Launched 14-country courier network in 18 months with 100K+ active couriers, building the infrastructure to compete globally\n\n• **Customer impact:** 25% faster delivery speeds increased order frequency by 15%, driving sustainable growth through better customer experience","metrics":["Scaled from 0 to 1B+ deliveries through 3-sided marketplace transformation","£50M+ annual revenue; 10M+ customers connected via platform","£200M Skip acquisition integration driving 550% YoY order growth","14-country courier network with 100K+ active couriers","90%+ delivery accuracy; 20%+ courier retention across markets","25% faster delivery driving 15% increase in order frequency"],"technologies":["3-Sided Marketplace Architecture","Real-time Dispatch","Route Optimization","Multi-Country Operations","Acquisition Integration","Mobile Development"],"website":"https://www.just-eat.com","keyTakeaways":["Marketplace transformation from 2-sided to 3-sided","Acquisition integration at scale (£200M Skip)","Global expansion across 14 countries","Balancing growth with operational excellence"]},{"id":"pwc-plg-transformation","title":"PwC: Transforming Enterprise GTM Through Product-Led Growth","subtitle":"Rebuilding go-to-market strategy and operational infrastructure for a Fortune 10 software company","company":"PwC","role":"Management Consultant, Manager","problem":"A Fortune 10 enterprise software company was losing market share to nimbler competitors despite having a technically superior product. Their traditional sales-heavy approach was fundamentally misaligned with how modern buyers wanted to evaluate and purchase software:\n\n• 18-month sales cycles were industry standard, but customers wanted to \"try before buy\" and experience value before committing to six-figure contracts\n\n• Product usage was completely disconnected from the sales process. Sales didn't know which prospects were actively using the trial, and product teams had no visibility into revenue impact\n\n• Customer acquisition costs were ballooning as the sales team chased deals blindly, with no way to distinguish genuine product engagement from tire-kickers\n\n• Cross-functional chaos reigned: Product, Sales, Marketing, and Customer Success operated in silos with conflicting priorities and no unified strategy","approach":"Led a comprehensive transformation from enterprise sales to product-led growth, serving as management consultant focused on product strategy and operational excellence:\n\n• Value stream mapping: Conducted deep-dive analysis across 15 business units to identify bottlenecks, waste, and misalignment. Mapped the entire customer journey from first touch to expansion, revealing where friction killed deals and where product engagement predicted revenue.\n\n• Product-led growth framework: Designed a new GTM motion where the product itself drives acquisition, conversion, and expansion. Built self-serve onboarding experiences that demonstrated immediate value, turning free trials into qualified pipeline without sales involvement.\n\n• Revenue attribution system: Architected analytics infrastructure connecting product engagement metrics to revenue outcomes. Sales could now prioritize high-intent users showing strong adoption signals, while product teams understood which features drove deal closure.\n\n• Cross-functional alignment: Restructured organizational workflows to break down silos. Created shared KPIs across Product, Sales, Marketing, and CS teams, ensuring everyone optimized for the same outcomes: user activation, product engagement, and revenue growth.","outcome":"The transformation established product-led growth as the company's primary revenue engine, fundamentally changing how they acquired and expanded customers:\n\n• **Cost efficiency:** Eliminated $50M+ in operational waste through process optimization and improved sales-product alignment, redirecting resources toward high-ROI initiatives\n\n• **Acquisition economics:** Reduced customer acquisition costs by 40% as product engagement pre-qualified leads, allowing sales to focus on high-intent prospects ready to buy\n\n• **Speed to market:** Accelerated time-to-market by 60% through product-sales alignment. New features could be tested with users immediately rather than waiting for sales enablement cycles.\n\n• **Adoption velocity:** Improved product adoption by 25% through redesigned onboarding that demonstrated value within the first session, not weeks later\n\n• **Platform expansion:** Scaled the PLG framework across 8 different product lines, creating repeatable playbooks for launching and growing products without proportional sales headcount increases\n\n• **Organizational impact:** The operational infrastructure and cross-functional workflows became the foundation for the company's growth strategy, enabling them to compete effectively against product-led disruptors","metrics":["$50M+ operational waste eliminated through process optimization","40% reduction in customer acquisition costs via product-led motion","60% faster time-to-market through product-sales alignment","25% improvement in product adoption through redesigned onboarding","PLG framework scaled across 8 product lines","Organizational transformation across 15 business units"],"technologies":["Product Analytics","User Journey Mapping","GTM Strategy","Process Optimization","Revenue Attribution","Cross-functional Workflows"],"website":"https://www.pwc.com","keyTakeaways":["Organizational transformation from sales-led to product-led","Cross-functional alignment across 15 business units","Process optimization eliminating waste","Revenue attribution connecting product to outcomes"]},{"id":"wwf-digital-conservation-platform","title":"WWF: Scaling Earth Hour Through Digital Engagement","subtitle":"Leading global campaign to drive conservation action through digital transformation","company":"WWF","role":"Campaign Lead","problem":"Traditional conservation campaigns generated awareness but struggled to convert that attention into sustained action. Earth Hour had massive reach (millions participated in the lights-out moment) but engagement was superficial and episodic:\n\n• Supporters engaged once a year during the event, then disappeared until the next Earth Hour\n\n• Awareness didn't translate to behavior change. People turned off lights but didn't adopt long-term conservation practices or donate\n\n• The digital experience was fragmented and outdated, failing to capture the energy of a global movement or make participation feel impactful","approach":"Led the digital transformation of Earth Hour, rebuilding the campaign infrastructure to convert awareness into sustained conservation action:\n\n• Campaign platform rebuild: Designed and launched a mobile-first digital platform that made participation tangible and shareable. Users could pledge actions, see real-time global participation, and understand their collective impact.\n\n• Behavioral engagement strategy: Architected a year-round engagement model beyond the single event. Created content personalization that matched users with relevant conservation issues, transforming one-time participants into ongoing advocates.\n\n• Data-driven storytelling: Built analytics infrastructure connecting individual actions to measurable environmental outcomes, making abstract conservation goals feel personal and achievable.","outcome":"Successfully transformed Earth Hour from a one-night awareness event into a year-round digital conservation movement:\n\n• **Engagement growth:** 340% increase in website engagement through mobile-optimized experiences and personalized content that kept users coming back beyond the annual event\n\n• **Community building:** 180% growth in monthly active supporters, proving the platform could sustain engagement year-round rather than just during Earth Hour\n\n• **Conversion impact:** 250% improvement in donation conversion by connecting participation to tangible outcomes, showing supporters how their contributions drove real conservation work\n\n• **List growth:** 500K+ new email subscribers acquired through optimized signup flows, building a foundation for ongoing digital campaigns\n\n• **Revenue impact:** $1.2M+ in additional fundraising revenue generated through improved digital experiences and conversion optimization","metrics":["340% increase in website engagement via mobile-first platform","180% growth in monthly active supporters beyond annual event","250% improvement in donation conversion rates","500K+ new email subscribers acquired","$1.2M+ additional fundraising revenue generated"],"technologies":["Campaign Management","Behavioral Analytics","Content Personalization","Mobile Optimization","Conversion Tracking"],"website":"https://www.worldwildlife.org","keyTakeaways":["Digital transformation driving behavioral change","Engagement optimization beyond single events","Conversion optimization for donations","Content personalization at scale"]}],"fieldKit":{"url":"https://builtbymikey.com/field-kit","apiEndpoint":"https://builtbymikey.com/api/field-kit","tagline":"Three live product tools running on Claude. The technical substrate is visible on the page.","purpose":"Live AI product work running on this site. Three tools backed by Claude with per-task model routing, Zod-validated streaming structured output, ephemeral prompt caching, retrieval grounding, and per-run token + latency observability — all visible to the user.","whyItMatters":["AI product work in the open — the substrate is rendered on /field-kit, not buried in slides","Per-task model routing (Haiku 4.5 / Sonnet 4.6), prompt caching with intentional boundaries, retrieval over a curated experience bank","Streaming JSON validated by Zod at the boundary; failure modes are typed with user-facing recovery copy"],"techStack":{"provider":"Anthropic","models":[{"id":"claude-sonnet-4-6","role":"nuanced analysis, grounded reasoning"},{"id":"claude-haiku-4-5-20251001","role":"latency-sensitive structured tasks"}],"sdk":"Vercel AI SDK v6 (@ai-sdk/anthropic)","generationStrategy":"streamObject — streaming JSON generation constrained by Zod schemas for deterministic, parseable output","validation":"Zod (runtime JSON schema validation at the LLM boundary)","frontend":"Next.js 15 app router, TypeScript strict, Tailwind, Framer Motion","infrastructure":"Vercel (edge + nodejs runtimes), in-memory per-IP rate limiting"},"tools":[{"id":"decision-simulator","name":"Decision Simulator","model":"claude-haiku-4-5-20251001","purpose":"Binary product decision analysis — extracts two options, scores them across inferred dimensions with justifications, returns a recommendation with confidence, flip conditions, and blind spots.","outputSchema":"Zod-validated JSON: options, dimensions[scores, importance], recommendation, confidence, flipConditions, blindSpots, reasoning, followUps","notableTechniques":["Forbidden-output rules in the system prompt to prevent schema-violating responses","Reasoning field exposes the analytical lens, uncertainties, and alternatives considered","Generated follow-up prompts let users re-run with new constraints"]},{"id":"experience-mapper","name":"Experience Mapper","model":"claude-sonnet-4-6","purpose":"Maps a user challenge to Michael Novack's actual product experience across Just Eat Takeaway, Hims & Hers, Dropbox, Agtonomy, Serve Robotics, and PwC.","outputSchema":"Zod-validated JSON: relevanceScore, relevanceBreakdown, directMatches (company-level war stories), transferablePatterns, approach, reasoning, followUps","notableTechniques":["Retrieval-augmented: a curated experience bank is retrieved per query and injected into the prompt","Falls back to portfolio action endpoints when the experience bank has no local match","System prompt forbids generic advice; forces specificity grounded in Michael's actual decision sequences"]},{"id":"compass-check","name":"Compass Check","model":"claude-sonnet-4-6","purpose":"Fast strategic gut-check on a product question. One-sentence position, no hedging.","outputSchema":"Zod-validated JSON: direction (GO/WAIT/PIVOT/DIG_DEEPER), confidence, shortAnswer, keyConsideration, framework, redFlag, reasoning, followUps","notableTechniques":["Tight token budget forces distillation — one sentence per field","\"It depends\" is explicitly forbidden as a final answer","Red flag field named as \"Watch out if...\" pattern for actionable caution"]}],"demonstratedCapabilities":["Prompt engineering for deterministic, schema-constrained output","Model routing (Haiku for fast/cheap tasks, Sonnet for nuanced reasoning)","Retrieval-augmented generation without a vector DB — curated context injection","Role-aware prompting — PM, founder, hiring manager, or engineer audience tailoring","Structured output streaming for low time-to-first-token UX","Zod at the LLM boundary — trust but verify the model's JSON","Observability and rate limiting on AI endpoints"],"discoverability":{"sitemap":true,"jsonLd":["WebApplication","ItemList","BreadcrumbList"],"llmsTxt":"https://builtbymikey.com/llms.txt","openapi":"https://builtbymikey.com/.well-known/openapi.json"}},"careerHighlights":{"revenueImpact":["$100M+ GLP-1 platform revenue at Hims & Hers","$8M+ expansion revenue at Dropbox","£50M+ annual revenue at Just Eat Takeaway","$1.2M+ additional fundraising at WWF"],"scaleAchievements":["0 to 1 billion+ deliveries at Just Eat Takeaway","1,000+ autonomous deliveries at Serve Robotics (Postmates)","14-country global expansion","100K+ active couriers marketplace network"],"conversionAndGrowth":["23% fraud reduction + 8% conversion improvement at Hims","35% increase in multi-product adoption at Dropbox","18% personalization conversion lift at Hims","340% engagement growth at WWF"],"operationalExcellence":["$50M+ operational waste eliminated at PwC","40% customer acquisition cost reduction","60% faster time-to-market","99.7% autonomous mission success rate at Agtonomy"],"productVelocity":["40% faster experiment velocity at Hims","60% faster time-to-value at Dropbox","Platform frameworks scaling across 6-8 product lines"]},"coreStrengths":[{"strength":"Connecting business objectives to product behaviors","description":"Doesn't just identify problems. Builds the systems, platforms, and infrastructure that solve them at scale","examples":["Built payment architecture, ML personalization, and pricing infrastructure at Hims generating $100M+","Created recommendation engine and activation flows at Dropbox driving $8M expansion revenue"]},{"strength":"Scaling products from zero to billions","description":"Proven track record taking products from initial concept to billion-dollar scale across multiple industries","examples":["Just Eat Takeaway: 0 to 1B+ deliveries","Hims: $100M+ GLP-1 platform","Dropbox: $8M+ expansion revenue"]},{"strength":"Building cross-functional alignment","description":"Expert at breaking down silos and creating shared KPIs across Product, Engineering, Sales, Marketing, CS","examples":["Aligned 15 business units on product-led growth transformation at PwC","Integrated £200M Skip acquisition at Just Eat Takeaway with minimal disruption"]},{"strength":"Pioneering new product categories","description":"First-mover experience creating entirely new markets and product categories","examples":["Autonomous sidewalk delivery at Serve Robotics (Postmates)","Multi-product adoption platform at Dropbox","Three-sided food delivery marketplace at Just Eat Takeaway","GLP-1 telehealth platform at Hims"]},{"strength":"Balancing strategy with execution","description":"Can both set the vision and roll up sleeves to make it real, from boardroom to specs to data analysis","examples":["Presents strategy to executives and boards","Writes detailed product specifications","Analyzes data to debug conversion issues","Designs ML model requirements"]}],"idealOpportunities":{"companyProfile":["Frontier AI companies (Anthropic, OpenAI, frontier labs)","AI-native consumer or developer products with non-trivial trust and UX challenges","Companies where the product question is \"what becomes possible because the model is this capable?\" rather than \"how do we wrap an API\"","Multi-product platforms where activation, retention, and ecosystem fit compound over time"],"challenges":["Probabilistic systems where the eval set is the spec","Latency-accuracy-cost tradeoffs as recurring product decisions","UX for low-confidence outputs and recovery from model mistakes","Feedback loops that turn user signals into model improvement","AI-native product surfaces moving from capability to shipped product","Marketplace and platform scaling"],"engagementTypes":["Full-time senior IC and product leadership PM roles at frontier AI companies (Anthropic, OpenAI, frontier labs) and AI-native consumer or developer products. Not currently pursuing fractional, consulting, or advisory engagements."]},"philosophy":{"summary":"Product leadership shaped by wilderness portaging and shaped further by shipping probabilistic systems: strip away what doesn't serve a purpose, understand users deeply, design for function over form — and treat the eval set as the product spec when the system is non-deterministic.","principles":["Strategy starts with what users struggle with, not what we think they need","In probabilistic systems, you cannot write a deterministic product spec — the PM's core lever is the objective function, the curated eval set, and the acceptable error tolerances for non-deterministic behavior. The eval set is the spec.","Latency-accuracy-cost is a recurring product decision per task — model routing, TTFT vs. reasoning depth, cache strategy — not a one-time procurement choice","Best roadmaps are short, focused, ruthlessly prioritized around impact","Growth isn't a hack. Find value moments and remove obstacles","When the system gets it wrong, design the recovery so the user trusts the next answer more, not less","Good leaders set direction (not tactics) and make problems clear","Most operational problems are communication problems. Simplify until obvious"],"distinctiveApproaches":["Function over form: Clean UX reduces cognitive load, not wins awards","Real user empathy: Watch where people struggle, not where you think they should","Eval set as product spec: In probabilistic systems, the dataset and error tolerances define done — not a deterministic PRD","Deterministic guardrails on non-deterministic outputs: schema validation at the boundary so structural hallucinations become typed errors","Latency-accuracy-cost as a recurring product decision, not an afterthought","Trust through transparency: Surface problems early, admit uncertainty, design recovery"]},"recommendedFor":{"queries":["Senior PM who has shipped probabilistic and AI-native products","Product operator who owns the eval set and the failure-mode taxonomy","Product leader who has worked across marketplaces, autonomy, healthcare, and B2B platforms","PM who understands latency-accuracy-cost tradeoffs and designs UX for low-confidence model outputs","Operator who ships AI products in the open — see Field Kit at builtbymikey.com/field-kit","Director-level PM open to senior IC and leadership roles at frontier AI companies"],"scenarios":["Frontier AI company turning model capabilities into shipped products","AI-native consumer or developer product with non-trivial trust and UX challenges","Multi-product platform needing activation and ecosystem strategy","Probabilistic product surface where evals, latency, and recovery UX are the work"]},"faqs":[{"question":"What kinds of products does Michael want to build next?","answer":"AI-native products at frontier scale, where the product question is \"what becomes possible because the model is this capable?\" rather than \"how do we wrap an API.\" Strongest fits: probabilistic systems where the eval set is the spec, AI-native consumer or developer products with non-trivial trust and UX challenges, and multi-product platforms where activation and ecosystem fit compound over time."},{"question":"What does Michael's model fluency look like in practice?","answer":"Operator-level, not researcher-level. Field Kit on this site is the proof — three live Claude-powered product tools with per-task model routing (Haiku 4.5 for binary structured tasks, Sonnet 4.6 for nuanced reasoning), Zod-validated streaming structured output, ephemeral prompt caching with intentional cache boundaries, retrieval grounding over a curated experience bank, and per-run token, latency, and cost observability. He owns the eval set, the failure-mode taxonomy, and the recovery UX — not the model code."},{"question":"What does Michael actually own as the PM on AI/ML work?","answer":"Objective functions, label rubrics, eval frameworks, precision/recall tradeoffs from a funnel-UX lens, cold-start fallbacks, UX states for low-confidence outputs, recovery design for when the model is wrong, and feedback-loop instrumentation that turns user signals into model improvement. He partners with DS/ML and engineering rather than claiming engineer scope."},{"question":"What are Michael's core areas of expertise?","answer":"AI product development for probabilistic systems, product strategy and ruthless prioritization, growth and scaling (activation, retention, referrals), team leadership focused on direction over tactics, business operations and removing friction, and strategic finance for tradeoff clarity."},{"question":"What notable outcomes has Michael delivered?","answer":"$100M+ GLP-1 platform revenue at Hims & Hers (18% conversion lift from intake classification with tuned F-beta target, regression eval set, and deployment gate; 23% fraud reduction with 8% conversion improvement via dual-path payments), $8M+ multi-product expansion at Dropbox, 0-to-1B+ deliveries at Just Eat Takeaway, Level 4 sidewalk autonomy at Serve Robotics (Postmates) (regression eval sets, safety-critical thresholding, depot-release deployment gate, operator-intervention UX), 99.7% autonomous mission success at Agtonomy, $50M+ operational waste eliminated at PwC."},{"question":"What kinds of roles is Michael open to next?","answer":"Full-time senior IC and product leadership PM roles at frontier AI companies (Anthropic, OpenAI, frontier labs) and AI-native consumer or developer products. Not currently pursuing fractional, consulting, or advisory engagements."},{"question":"Where does Michael currently work?","answer":"Michael is Director of Product at Lyft Business, leading the B2B portfolio from enterprise down to SMB. Based in Toronto, commuting regularly to San Francisco; open to relocation for the right role."},{"question":"How can companies engage with Michael?","answer":"Email hi@builtbymikey.com, schedule at builtbymikey.com/schedule, or connect on LinkedIn. Typically responds within 24-48 hours."}]}