Data Scientist

Role Overview

Translate complex business problems into predictive models and analytical frameworks that drive decision-making, personalization, and operational efficiency. Work at the intersection of statistics and engineering to ship production-grade data products.

Responsibilities

• Design, build, and deploy predictive models and statistical algorithms to improve user experience, retention, and conversion metrics.
• Develop and maintain data pipelines in SQL and Python to extract, clean, and prepare features for training.
• Define experimentation frameworks and run rigorous A/B testing, including:
  • sample size estimation and power analysis
  • post-hoc segmentation
• Conduct exploratory data analysis (EDA) to uncover growth opportunities, product frictions, and behavioral patterns in large datasets.
• Partner with engineering to integrate offline models into real-time production serving systems and monitor post-deployment performance.
• Communicate results to stakeholders via clear documentation and dashboards.

Requirements

• 3–6 years of professional experience as a Data Scientist or in a quantitative analytics role (preferably in high-growth tech).
• Strong Python skills (including pandas, numpy, scikit-learn) and advanced SQL.
• Deep understanding of statistics, experimental design, regression, and ML methods (classification/clustering).
• Experience with cloud data warehouses (e.g., Snowflake, BigQuery) and BI/reporting tools (e.g., Tableau, Looker).
• MS or PhD in a quantitative field (CS, Statistics, Mathematics, Economics, or Physics).

Bonus / Nice to Have

• PySpark or distributed computing experience.
• Familiarity with containerization tools like Docker and Kubernetes.