Raya
Overview
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.
Benefits 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.
Responsibilities
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
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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.
Benefits 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.
Responsibilities
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
#J-18808-Ljbffr