Job description
We’re looking for a Senior Data Scientist to deepen our understanding of how models and product experiences impact user outcomes. You’ll work closely with Data Science, Machine Learning Engineering, and Analytics teammates on the recommendations and search systems that power personalized experiences.
We prioritize learning and teamwork and love giving people the opportunity to champion big challenges and grow into better versions of themselves. A great candidate is excited to grow our use of machine learning, AI, and other sophisticated algorithms to build a better user experience. Finally, they believe in Raya’s vision, which is to enrich lives by fostering relationships through quality, in person interactions.
We offer comprehensive medical and dental coverage, $50 a day food delivery budget, equity based employment, a great culture, learning opportunities, unlimited vacation, 12 weeks paid parental leave, and we pay all employees $1,000 a year to go somewhere in the world that they’ve never been because of our values of human connection, empathy, and curiosity.
In this role, you will:
- Run causal and impact analysis for experiments and product changes, helping the team understand not just what happened, but why
- Design and maintain measurement frameworks for key systems like recommendations, ranking, and personalization
- Partner with ML Engineers to evaluate model performance and offline metrics, and to develop scalable evaluation pipelines
- Work closely with Analytics to ensure high-quality experimentation and metric consistency, supporting clear, reliable insights for product and model improvements
- Dig into user behavior and engagement patterns to generate insights and hypotheses that shape product direction
- Contribute to analytical data workflows – for example, defining custom metrics or refining evaluation tables to improve reproducibility and self-serve capability
- Communicate clearly and proactively with product and engineering partners, bringing clarity to complex systems and metric
Qualifications
- 4–7 years of experience in data science, analytics, or a related quantitative field (product or ML-facing experience preferred)
- Strong SQL and Python skills, with experience in pandas, NumPy, and data visualization libraries
- Deep understanding of causal inference and experimentation, including methods such as CUPED, diff-in-diff, matching, or propensity-based estimators
- Experience designing and interpreting A/B tests and translating results into product recommendations
- Experience with model tuning and evaluation as well as common ML libraries (e.g. scikit-learn, XGBoost)
- Proficiency with modern data tooling, such as dbt and Snowflake/Databricks for transformation and modeling; Mixpanel or Segment for product instrumentation; and Looker, Omni, or Tableau for visualization
- Familiarity with off-policy evaluation and counterfactual analysis methods (e.g., inverse propensity scoring, doubly robust estimation) used to evaluate recommender or personalization models offline.
- Strong communication and storytelling skills, with the ability to align stakeholders on insights and measurement strategy
- Comfortable working cross-functionally with ML Engineering, Analytics, Product, and Infrastructure teams
