Data Scientist

Job Description

Summary

We’re looking for a Data Scientist who thrives on solving ambiguous product problems with statistical rigor and production-grade solutions. This role isn’t about cranking out dashboards or tuning off-the-shelf models — it’s about collaborating directly with product and engineering to shape what we build, why we build it, and how we measure its impact. You’ll be expected to lead the design of experiments, prototype forecasting tools, and help scale data-informed decision-making across the company. If you’ve ever spotted a misleading metric and redesigned the tracking schema to fix it, you’ll fit right in.

What You’ll Do

• Own the end-to-end design and analysis of product experiments (A/B, multivariate), including power analysis, lift calculations, segmentation, and post-hoc diagnostics.
• Develop custom statistical models to forecast behavioral metrics (e.g., churn, engagement decay curves) and help product teams make proactive decisions.
• Build scalable feature stores in collaboration with data engineering, optimized for latency and reusability in production ML pipelines.
• Partner with PMs and designers to embed data instrumentation into feature specs before launch — no backfitting.
• Design and maintain Bayesian models for user segmentation and lifetime value estimation, deploying model updates weekly via Airflow.
• Identify failure modes in existing ML models (e.g., feature drift, selection bias) and lead cross-functional retrospectives with model owners.
• Present technical findings to executive leadership, emphasizing actionability over complexity.

What You Bring

• Proven experience with causal inference (e.g., propensity score matching, inverse probability weighting, difference-in-differences).
• Strong SQL fluency, and Python expertise with libraries such as statsmodels, pymc, or scikit-learn.
• Familiarity with real-time analytics (e.g., using Apache Kafka or Flink for event stream modeling).
• Experience with feature selection and model interpretability methods (e.g., SHAP values, permutation tests).
• Experience contributing to data quality standards and establishing SLAs for downstream dashboards and services.
• A portfolio of prior work (e.g., GitHub repo, published analysis, internal tooling) that demonstrates how your work has influenced product or user experience.

Nice to Have

• Experience designing or refining online experimentation platforms.
• Prior work in marketplaces, logistics, or personalization systems.
• Exposure to differential privacy or federated learning techniques.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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