About the Role
Lead data-driven experimentation: Design, execute, and analyze A/B tests to evaluate product and feature impact on engagement, retention, and customer satisfaction.
Drive product insights: Conduct deep-dive analyses on user journeys, feature adoption, and product funnels to identify opportunities for growth and optimization.
Develop predictive models: Apply machine learning and statistical techniques to forecast behavior, personalize user experience, and reduce churn.
Elevate analytics quality: Partner with engineering and data platform teams to ensure high-quality telemetry data, reliable pipelines, and consistent business definitions.
Collaborate cross-functionally: Work closely with Product, UX, and Engineering to translate insights into actionable product improvements.
Mentor and guide junior data scientists and analysts, promoting best practices in experimentation design, causal inference, and data storytelling.
Communicate insights effectively to product and executive stakeholders through clear, visual narratives that drive decision-making.
About You
5–8 years of industry experience in data science, experimentation, or product analytics.
Proven track record of applying causal inference methods such as difference-in-differences, matching, regression discontinuity, or instrumental variables.
Strong experience with A/B testing design, implementation, and interpretation.
Proficient in Python (pandas, scikit-learn, stats models) and SQL; experience with data visualization tools (Power BI, Tableau, or Looker) preferred.
Solid understanding of product telemetry data and experience analyzing user engagement, adoption, or retention patterns.
Experience developing and deploying machine learning models in product or business contexts.
Excellent problem-solving, communication, and collaboration skills — comfortable influencing decisions at all levels.
Bachelor’s or master’s degree in a quantitative field (Statistics, Computer Science, Engineering, Mathematics, or related) is preferred.
#LI-Hybrid
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