Data Scientist

Role Overview

Build and deploy advanced statistical and machine learning models that turn raw data into actionable insights. You’ll develop predictive pipelines, run rigorous A/B tests (including power analysis and sample sizing), and partner with product, data engineering, and business leaders to optimize key product features.

Responsibilities

• Design, build, and deploy predictive models using supervised and unsupervised machine learning to improve user retention and engagement.
• Develop and execute A/B testing frameworks (power analysis, sample size determination, and multi-touch attribution modeling).
• Build clean, scalable data pipelines in SQL and PySpark for feature store extraction, transformation, and loading.
• Create model monitoring pipelines to detect feature drift, concept drift, and performance degradation in real-time production.
• Translate analytical and statistical findings into clear, actionable recommendations for leadership.
• Write modular, version-controlled Python code (Git) and participate in peer code reviews.

Requirements

• 3–6 years of experience as a Data Scientist or Applied Statistician in a product-focused, high-growth environment.
• Strong Python skills: pandas, NumPy, scikit-learn, and statsmodels.
• Advanced SQL and ability to write complex, optimized queries.
• Deep understanding of statistical inference, hypothesis testing, regression analysis, and experimental design.
• Experience with cloud data warehouses: Snowflake, BigQuery, or Redshift.
• Experience with BI visualization tools such as Tableau or Looker.
• M.S. or Ph.D. in a quantitative field (Statistics, Applied Mathematics, Computer Science, Economics, or related).

Nice to Haves

• MLOps experience with MLflow, Prefect, or Airflow.
• Deep learning frameworks: PyTorch or TensorFlow.