Senior Data Scientist (Growth)

At Inflection AI, our public benefit mission is to harness the power of AI to improve human well-being and productivity. The next era of AI will be defined by agents we trust to act on our behalf. We’re pioneering this future with human-centered AI models that unite emotional intelligence (EQ) and raw intelligence (IQ)—transforming interactions from transactional to relational, to create enduring value for individuals and enterprises alike. Our work comes to life in two ways today: Pi, your personal AI , designed to be a kind and supportive companion that elevates everyday life with practical assistance and perspectives. Platform — large-language models (LLMs) and APIs that enable builders, agents, and enterprises to bring Pi-class emotional intelligence into experiences where empathy and human understanding matter most. We are building toward a future of AI agents that earn trust, deepen understanding, and create aligned, long-term value for all. About the Role As a Senior Data Scientist on our Product team, you will be the primary architect of our understanding of user behavior. Reporting directly to the Head of Data Analytics, you will serve as the dedicated data science partner for our Product and Growth teams. Your mission is to go beyond descriptive analytics — building predictive models and behavioral frameworks that allow us to anticipate user needs, identify friction before it becomes churn, and surface the signals that drive long-term retention. This is a high-leverage, deeply technical role. You will own the full analytical lifecycle: from schema design and data quality through feature engineering, modeling, and insight delivery. You will be the bridge between our Data Engineering team and our Product organization, ensuring the infrastructure we build supports ambitious, model-driven growth decisions. You will also play a key role in understanding how different user audiences discover, adopt, and derive value from our product — connecting acquisition signals to downstream behavioral outcomes to build a complete picture of growth. What You'll Do Model User Behavior Predictively: Build and deploy models that forecast retention, churn risk, and engagement trajectories — moving from reactive reporting to proactive intervention. Identify Leading Indicators: Discover early behavioral milestones — the "Aha! moments" — that predict long-term user success, using cohort analysis, survival modeling, and feature importance techniques. Drive Experimentation: Design, implement, and analyze A/B tests across product features, providing the statistical rigor needed to inform high-stakes launch decisions. Understand Audience-Level Performance: Analyze how different user segments and acquisition cohorts behave over time — identifying which audiences activate fastest, retain longest, and signal the highest long-term value. Translate these insights into actionable guidance for product and growth strategy. Own Attribution Growth Measurement: Maintain the data bridge between acquisition channels, campaigns, and in-product behavior, ensuring we can reliably connect where users come from — and what brought them — to how they succeed. This includes working with tools like AppsFlyer and PostHog to ensure attribution data is accurate, consistent, and analytically useful, and that campaign-level performance can be evaluated against meaningful downstream outcomes, not just top-of-funnel metrics. Build the Behavioral Data Foundation: Manage the flow of data from our production Postgres environment and behavioral tools into Snowflake, building the transformation layers (dbt) that power self-serve analytics and model feature pipelines. Define Strategic Metrics: Partner with Product leads to define North Star metrics and develop the input/output maps that connect day-to-day product decisions to long-term growth outcomes. Combat Churn Proactively: Develop deep, model-driven insights into engagement patterns to identify at-r ... (truncated, view full listing at source)