Hammerhead
Recruited as the first hardware product manager to lead post-acquisition integration of Hammerhead into SRAM. Led the Karoo product line from Karoo 2 through the 2024 generation — unifying hardware and software product vision for the first time and developing the first 4+ year integrated HW/SW platform strategy. Launched with 56% YoY revenue growth and zero critical bugs in first 90 days.
Karoo Connected Platform
Color Shell Program
Hammerhead Mounting System
Trek Bicycle
Thirteen years at Trek (2009–2022) — from forecasting and supply chain analytics to Road Bicycle Product Manager to Program Manager of Project One custom bikes. $350M portfolio across 1,500+ SKUs, multi-tier product platforms from entry aluminum to $15K+ configured-to-order. Delivered 6 major product platforms driving 20%+ growth.
Domane Endurance Family
Speed Concept TT/Tri
Scale & Profitability
Not a Bicycle — a Race System
Every component engineered to add zero aero drag. Five distinct domains that all coexist in one platform.
Five integrated engineering domains in one race machine
Integrated bento box — 8+ gels, zero aero penalty
Modular cockpit — thousands of valid fit configurations
Aero downtube bottle — 700ml, flush-mount cage
Hidden flat kit — CO₂, tube, tools inside the downtube
Jobs to Be Done
Leading full-cycle product discovery — from generative research through feature definition — for a connected device expanding beyond the typical road cyclist into underserved rider segments.
We were expanding beyond our core user. We needed to understand why riders were underserved.
The cycling computer market was dominated by road-focused devices. An entire segment of riders — including mountain bikers — had needs that weren't being met. We set out to answer fundamental questions: How do they use data and technology? What differentiates their experience? And what are the highest-value problems nobody is solving?
A structured approach to messy discovery
I designed and led a 5-phase research process that balanced rigor with the creative messiness that good discovery requires. Cross-functional collaboration across hardware PM, software PM, industrial design, hardware engineering, software engineering, and product design.
Cross-functional collaboration across HW & SW
From conversations to conviction
Jobs-to-be-Done
Uncovering what riders are really trying to accomplish — not what features they want, but the underlying jobs they hire products to do.
Think, Feel, Act
Riders in real scenarios: What are you THINKING? What do you FEEL? What do you DO next? Revealing emotional and behavioral patterns standard interviews miss.
Outcome Statements
Every interview synthesized into measurable expressions of what riders want to achieve. These became the backbone of the entire program.
Job Map: Navigation While Riding
Determine trail preferences and conditions before heading out
Find trails and routes that match criteria, planned or on the fly
Set up device, load route, confirm key trail details
Verify correct trail and heading the right direction
Navigate in real-time with minimal distraction
Stay aware of upcoming trail features and turns
Adjust route on the fly without stopping
End ride and review for future planning
Quantifying opportunity at scale
Not all problems are equally important — to customers or to the business. The I/S framework surfaces the problems with the highest importance and lowest satisfaction. Those are the highest-value opportunities to build for.
Importance vs. Satisfaction
Lower-right quadrant = high importance, low satisfaction. These are problems riders care deeply about that nobody is solving well — the highest-value opportunities for the product.
Illustrative. Green dots highlight high-importance, low-satisfaction outcomes — the opportunity zone.
Bringing research to life with cross-functional ideation
I organized and facilitated a multi-day cross-functional workshop that brought together product management, industrial design, hardware engineering, software engineering, and product design. The workshop was carefully structured around three inputs: research-backed rider archetypes, prioritized outcome statements from the I/S analysis, and a technology landscape prepared by engineering.
Rider Archetype Cards
The Data Optimizer
- great grasp on the digital bike experience
- experiments with the latest digital tech
- knows all the faults of current solutions
- wants to see where AI can take cycling
- safety first; radar is critical
The Hands-On Builder
- wants the best gear, doesn't care where it's from
- a tinkerer by nature
- does their own bike building and maintenance
- interested in the mechanical and hands-on perspective
The Professional Racer
- weight weenie
- has all equipment gifted / given to them
- has a team behind them
- doesn't ride with a phone
The Endurance Seeker
- solo rider, has a coach
- may do some racing but primary competition is with themselves
- not super interested in bike maintenance
The Tastemaker
- aesthetics matter as much as performance
- active on social media, shares ride content
- curates their setup carefully
The Social Rider
- rides for fun, not training
- group ride organizer
- values navigation and trail discovery
- uses technology casually
Workshop in Action
The workshop used a structured Figma environment where cross-functional teams worked through prioritized outcome statements, generated solutions, and evaluated them against feasibility constraints in real time.
Full workshop environment — research inputs, ideation boards, and feature analysis across multiple sessions
Solution analysis and bundling — cross-functional teams evaluating and prioritizing feature concepts
The riders we designed for
From 100+ interviews and 2,500+ survey responses, we distilled the mountain bike audience into distinct personas.
“I go out mountain biking and it's a nice way to connect with nature and free my mind.”
The Weekend Explorer
Rides mid-range equipment, doesn't modify it. Social riding is the main draw. The group text thread matters more than the GPS.
Not overly technical. Checks weather on a browser. Appreciates simplicity and low-maintenance tech.
“I'm a competitive person. I enjoy pushing myself using any and all tools to get a marginal gain.”
The Competitive Edge
Life revolves around bikes. Competitive racer using coaches and structured training. Splits time between road and off-road.
Borderline data nerd. Monitors HR, power, glucose, gear position. Post-ride analysis is as important as in-ride data.
“Biking came at a time where my mental health was at a low level. It brought me out of that.”
The Late Convert
Started riding during a health crisis. Now fits biking into everything. Loves long XC trails. Bought gear on marketplace forums.
Relies on a cycling computer for tracking and safety. Prefers post-ride analysis over live monitoring. Did deep forum research before buying.
“I want to see where AI can take cycling. I know all the faults of my current setup.”
The Early Adopter
Great grasp on the digital bike experience. Experiments with the latest tech. Active in online communities. Provides detailed manufacturer feedback.
Early adopter. Runs beta firmware. Has opinions on protocols. Wants open APIs and customization. Radar and crash detection are non-negotiable.
From insight to product vision
The workshop's repeatable 4-step process — connecting user stories from research directly to engineering feasibility checks in real time.
Story
A specific user scenario grounded in research
Sketch
Quick visual exploration, low-fidelity, many options
Feature Def
Refined concept with clear scope and acceptance criteria
Eng. Check
Feasibility validation with implementing partner
What this work delivered
Deep understanding of an underserved market
100+ interviews generating 383 insights and 2,500+ survey responses replaced assumptions with evidence across the organization.
100+ outcome statements ranked by opportunity
The I/S framework gave the team a shared, data-driven language for prioritization — eliminating opinion-based debates.
Engineering, Design, and Product speaking the same language
By the time we reached feature definition, every function already had deep context from shared research.
383 insights drove 26+ hardware and software features
An executive summary connecting every feature back to a quantified rider need — ready for the Define phase.
A discovery framework the org can reuse
The 5-phase process and feature workflow became the standard for subsequent programs.
This case study describes methodology and process. Specific product details, data, and business strategy remain confidential.
Builder
Product leaders should leverage the latest tools. I've built and deployed apps and AI agent systems leveraging LLMs to automate workflows, system monitoring, and command execution. Deep experience applying prompt engineering and structured output design to product requirements, user research synthesis, and development workflows.
ClawDeck
MIDIControl
OpenClaw
Let's Talk
Product
I keep a short calendar and I'm direct. If the role is interesting, we'll know within 30 minutes.