Data Scientist

Role Overview

Build and scale advanced statistical models and predictive algorithms that turn complex data into actionable product strategies and automated decision systems.

Responsibilities

• Design, prototype, and scale ML models using XGBoost, LightGBM, and scikit-learn (e.g., churn, lifetime value prediction, recommendations).
• Develop and analyze experiments using A/B testing and multivariate testing to evaluate product changes and algorithm updates.
• Build data pipelines and feature engineering workflows in SQL, Python, and PySpark for large-scale structured and unstructured data.
• Partner with Product Management and Engineering to translate business requirements into production-ready data science specifications.
• Establish model monitoring for feature drift, concept drift, and performance degradation.
• Communicate model results and performance metrics to technical and non-technical stakeholders.

Requirements

• 3–6 years experience as a Data Scientist or Quantitative Analyst, delivering production ML and experimentation frameworks.
• Strong Python and SQL skills; hands-on experience with pandas, NumPy, scikit-learn, and statsmodels.
• Solid statistical foundation: hypothesis testing, regression, experimental design, and causal inference.
• Experience with cloud/distributed platforms: AWS, Snowflake, and Spark.
• Master’s or PhD in a quantitative field (Statistics, CS, Economics, Mathematics) or equivalent practical experience.

Bonus / Nice-to-haves

• Deep learning experience (PyTorch, TensorFlow).
• Experience deploying models using FastAPI and Docker.