Design, implement, and analyze A/B tests, multi-armed bandits, and quasi-experimental methods to measure the impact of product changes.
Apply causal inference techniques (e.g., difference-in-differences, propensity score matching, synthetic control, regression discontinuity) to estimate treatment effects in non-randomized settings.
Collaborate with product, engineering, and marketing teams to define hypotheses, success metrics, and statistical power requirements.
Ensure rigorous statistical validity (e.g., controlling for biases, multiple testing corrections, confidence intervals).
Develop and refine retention measurement frameworks (e.g., cohort analysis, survival analysis, churn prediction).
Define and track core engagement metrics (DAU, WAU, MAU, rolling retention, N-day retention) and diagnose trends.
Identify key drivers of retention through segmentation, funnel analysis, and predictive modeling.
Work with growth teams to optimize onboarding, engagement loops, and monetization strategies.
Build and maintain scalable data pipelines (using PySpark, SQL, or big data tools) to process and analyze large datasets.
Develop automated dashboards and reports (e.g., Tableau, Looker, Metabase) to monitor experiment performance and retention trends.
Ensure data quality and consistency in metric definitions across teams.
Optimize queries and computations for performance and cost efficiency in distributed systems (e.g., Databricks, AWS EMR, GCP BigQuery).
Partner with product managers, engineers, and marketers to translate business questions into data-driven analyses.
Present findings and recommendations to executive stakeholders in clear, actionable formats.
Mentor junior data scientists and analysts on best practices in experimentation and retention analytics.

Requirements

3+ years of experience in data science, analytics, or experimentation (or equivalent in academic research).
Strong background in statistics and causal inference (hypothesis testing, Bayesian methods, experimental design).
Hands-on experience with SQL and Python (Pandas, NumPy, SciPy, StatsModels, Scikit-learn).
Proficiency in experimentation tools (e.g., Optimizely, Statsig, Eppo, or custom in-house systems).
Experience defining and analyzing retention metrics (DAU/WAU/MAU, cohort retention, churn).
Familiarity with big data tools (PySpark, Hadoop, or similar distributed computing frameworks).

Benefits

Comprehensive health, dental, vision, and additional support programs.
The opportunity to work on cutting-edge AI with a small, mission-driven team.
A culture that values transparency, trust, and community impact.
Visa sponsorship available.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

A/B testingcausal inferencedifference-in-differencespropensity score matchingregression discontinuitycohort analysissurvival analysischurn predictionSQLPython

Soft Skills

collaborationcommunicationmentoringanalytical thinkingproblem-solving