Requirements

7+ years of experience in data science, product analytics, or related field with a record of influencing product decisions
Strong foundation in statistics, including probability, inference, hypothesis testing, regression, and understanding bias and confounding
Practical experience designing and analyzing A/B tests with metrics definition, power assessment, experiment validity, and result interpretation
Advanced SQL skills and strong proficiency in Python for data exploration, analysis, and reproducible workflows
Working knowledge of dbt, data modeling, modern data platforms, and orchestration tools like Dagster; understanding of ETL pipelines and dependencies
Hands-on experience with AI coding tools such as Codex, Claude Code, or Cursor, with good judgment to verify correctness and maintain code quality
Experience partnering directly with product teams, ideally consumer products, to understand behavior and improve activation, engagement, retention, or monetization
Ability to independently structure ambiguous problems, prioritize work, and communicate recommendations to technical and nontechnical partners
Curiosity, initiative, and willingness to learn unfamiliar systems and follow through on useful work

Nice to Haves

Experience working with Snowflake including querying large datasets and understanding query performance and cost
Experience with crypto, tokenized assets, finance, or fintech

Analyzing user journeys, activation, engagement, retention, and monetization to identify opportunities and recommend improvements
Designing and evaluating A/B tests including hypotheses, success metrics, guardrails, sample sizing, and interpretation, communicating uncertainty and causal effects
Defining and maintaining product metrics; partnering with Engineering to improve instrumentation and validate data
Building reusable analytical datasets and dbt models using SQL; collaborating on Snowflake, Dagster, and ETL pipelines to improve data quality
Using Python for statistical analysis, modeling, and automation; ensuring work is reproducible, documented, and maintainable
Incorporating AI coding tools into workflow to accelerate development and analysis; reviewing and validating generated code and results
Leading ambiguous problems independently from question through analysis, recommendation, and measurement; communicating clearly and influencing priorities