Data Scientist — Los Angeles, CA

About The Role

The role sits at the intersection of statistical rigor and production impact - designing experiments, building predictive models, and translating messy real-world data into decisions that shape product direction.

Working closely with ML engineers and product teams, the Data Scientist owns analyses and models end-to-end: from hypothesis framing and data exploration through deployment, monitoring, and iteration.

Key Responsibilities

• Design and analyze controlled experiments (A/B tests), including power analysis, variance reduction techniques, and guardrail metric definition

• Build and validate predictive models (causal inference, forecasting, classification) in Python using scikit-learn, XGBoost, and PyTorch

• Develop and maintain large-scale data pipelines in SQL and PySpark, ensuring data quality and reproducibility across training and reporting workflows

• Partner with ML engineers to productionize models, defining feature definitions, evaluation metrics, and monitoring thresholds

• Define and track core product metrics; build dashboards and self-serve analytics in Tableau, Looker, or Mode for cross-functional stakeholders

• Present findings and recommendations to engineering and product leadership with clear statistical reasoning and quantified business impact

• Contribute to the team's experimentation standards, documentation, and analytical tooling

What We Are Looking For

• 3–6 years of experience in data science, applied analytics, or a related role at a product-driven technology company

• Expert-level SQL and strong Python; hands-on experience with pandas, scikit-learn, and at least one deep learning or gradient boosting framework

• Demonstrated experience designing and analyzing A/B tests at scale, including experimentation platforms and statistical pitfalls (novelty effects, interference, multiple comparisons)

• Experience with distributed data processing using Spark or similar big-data tooling

• MS or PhD in Statistics, Computer Science, Economics, or a related quantitative field (or equivalent industry experience)

• Strong communication skills - able to explain technical tradeoffs to non-technical stakeholders and influence product decisions

• Bonus: experience with causal inference methods (diff-in-diff, synthetic control, uplift modeling), dbt, or building production ML models end-to-end