Senior Data Scientist (Growth)

Inflection AI • Palo Alto, CA

3h

About The Position

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.

Requirements

• 5–8 years of experience in product data science, growth analytics, or a related quantitative role, with demonstrated impact driving product and growth decisions through experimentation and data analysis
• Strong Product & Growth Analytics Foundation: Deep experience with funnel analysis, cohort modeling, experimentation, and behavioral event data — you understand how users move through a product, why they stay, and how acquisition source and campaign context shape long-term outcomes. Familiarity with mobile attribution frameworks (e.g., AppsFlyer, Adjust) is a plus.
• Predictive Modeling Expertise: Hands-on experience building and validating ML/statistical models in a production or near-production context — churn models, propensity scores, LTV forecasting, or similar user behavior applications.
• Technical Mastery: Expert-level SQL and hands-on experience with modern data warehouses (Snowflake) and event-based tracking tools (PostHog, Segment, or similar). Proficiency in Python for data modeling and analysis.
• Strategic Mindset: Proven ability to translate messy behavioral data into a clear narrative, identifying not just what is happening, but why and what to do next.
• Data Quality Advocate: A pragmatic approach to data governance — you are as comfortable debugging a broken tracking tag as you are presenting model results to leadership.
• Startup Agility: An innovation-focused mindset that thrives in a high-velocity environment where you are both the strategist and the executor.
• Academic Foundation: A bachelor's degree or equivalent in a quantitative field (e.g., Computer Science, Statistics, Economics, or Engineering).

Responsibilities

• 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-risk users and surface actionable signals before churn impacts the bottom line.

Benefits

• Diverse medical, dental and vision options
• 401k matching program
• Unlimited paid time off
• Parental leave and flexibility for all parents and caregivers
• Support of country-specific visa needs for international employees living in the Bay Area

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 11-50 employees