AI Feature Adoption Cohort Segmentation Developer

Fynix Studio

Remote · $80k – $100k / year · Full-time · Posted 4 months ago

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About the role

Not all users who adopt a feature behave the same way. The power user who uses the feature 20 times per week is in a fundamentally different cohort than the casual user who tried it once after seeing it mentioned in a changelog. Understanding the behavioral segmentation of feature adopters — and which segment a new user is likely to land in — is how product teams design effective activation sequences and predict feature retention. Our product analytics platform serves 200 SaaS companies. Feature adoption cohort segmentation is a mid-level engineering challenge with direct product impact: the models we build tell product managers not just how many users adopted a feature, but which behavioral archetypes their adoption base consists of and how those archetypes differ in long-term retention, expansion revenue, and advocacy behavior. You'll own the cohort segmentation system: clustering feature adopters by behavioral patterns, building predictive models that classify new users into segments based on early adoption signals, and creating the segmentation insights that product teams use to design differentiated activation flows for different adopter archetypes.

Fully remote.

Responsibilities

Build behavioral clustering models segmenting feature adopters by usage pattern archetypes
Implement early-signal predictive models classifying new users into adoption segments
Develop segment profile descriptions connecting behavioral patterns to product engagement outcomes
Create segment transition tracking detecting when users shift between adoption archetypes over time
Build segmentation reporting APIs consumed by client product dashboards
Validate segment stability and predictive validity across product update cycles

Requirements

3+ years in product analytics, data science, or growth engineering
Strong Python and SQL skills with clustering and classification model experience
Understanding of SaaS product metrics, feature adoption, and user lifecycle analysis
Experience building segmentation systems that drive actionable product decisions
Ability to communicate segment findings clearly to non-technical product managers