Data Scientist

Role Overview

Design and implement statistical models and machine learning algorithms that power core decision-making systems. You’ll bridge raw data pipelines and actionable product intelligence, translating business challenges into scalable mathematical formulations and deploying models that influence user acquisition, engagement, and retention.

Responsibilities

• Develop, evaluate, and iterate on predictive models and causal inference frameworks using Python, SQL, and scikit-learn.
• Design and run rigorous A/B testing: sample sizing, power calculations, and post-hoc segmentation.
• Build and maintain scalable feature engineering pipelines in Snowflake, BigQuery, or Redshift using dbt and SQL.
• Communicate insights with product recommendations and visualizations using Tableau, Looker, or Streamlit dashboards.
• Collaborate with platform engineers to deploy models via API endpoints or batch inference, including ongoing monitoring for performance and drift.

Requirements

• 3–6 years of experience as a Data Scientist or Quantitative Analyst, with a track record of driving business metrics through model deployment.
• Strong Python skills (pandas, NumPy, scikit-learn) and advanced SQL (window functions, CTEs, query optimization).
• Solid statistical foundations: hypothesis testing, regression, experimental design, and causal inference.
• Experience with relational databases and data warehouses (Snowflake, BigQuery) and modern data stack tooling like dbt.
• Master’s or Ph.D. in Statistics, Applied Mathematics, Economics, Computer Science, or a related quantitative field.

Bonus

• Experience with Bayesian modeling (e.g., PyMC) or deep learning libraries such as PyTorch for sequence modeling.