Hybrid Data Scientist

at People Culture Talent

• Job title: Data Scientist
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $200,000 - $400,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, BigQuery, Distributed Systems, Google Cloud Platform, Hadoop, Numpy, Pandas, PySpark, Python, Scikit-Learn, SQL, Tableau
• Location requirements: Hybrid, San Francisco, United States

About the role

• Data Scientist optimizing user engagement through experimentation and causal inference in AI performance evaluation. Collaborating across teams to provide actionable insights and develop scalable data pipelines.

Responsibilities

• 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.