Data Scientist

Experimentation & Causal Inference

• 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).

Retention & Engagement Analytics

• 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.

Data Infrastructure & Scalable Analytics

• 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).

Cross-Functional Collaboration

• 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).

Highly Desirable

• Expertise in PySpark for large-scale data processing and analytics.
• Experience with time-series forecasting, survival analysis, or uplift modeling.
• Knowledge of ML for retention (e.g., propensity models, clustering, recommendation systems).
• Experience with data visualization tools (Tableau, Looker, Plotly, Matplotlib/Seaborn).
• Background in growth analytics, product analytics, or marketing analytics.

Nice to Have

• Advanced degree (MS/PhD) in Statistics, Economics, Computer Science, or a quantitative field.
• Experience with reinforcement learning or bandit algorithms for dynamic experimentation.
• Knowledge of MLOps or productionizing models (e.g., MLflow, Airflow, Docker).