Data Scientist

Role Overview

You will develop advanced statistical and machine learning models to improve operational efficiency and personalize user experiences. You’ll transform complex raw data into predictive frameworks that integrate with the core product, partnering with MLOps and backend engineering to productionize analytical solutions.

Responsibilities

• Design, build, and deploy predictive models using statistical, machine learning, and deep learning techniques
• Clean, aggregate, and analyze high-dimensional data from disparate sources using SQL, Pandas, and PySpark for feature engineering
• Validate and evaluate models using rigorous protocols, including:
  • offline simulation
  • online A/B testing to measure business impact
• Collaborate with MLOps to containerize models with Docker and package them into APIs for production integration
• Create automated visualization dashboards and technical documentation for cross-functional stakeholders
• Monitor live performance with metrics to detect data drift and concept drift, triggering automated retraining pipelines

Requirements

• 3–6 years experience as a Data Scientist or Quantitative Analyst, with a record of deploying models to production
• Strong Python skills and statistical/data science libraries: pandas, NumPy, scikit-learn, Statsmodels
• Expert SQL skills for complex queries, transformations, and performance tuning at scale
• Solid grounding in statistical theory, experimental design (A/B testing), hypothesis testing, and regression diagnostics
• Bachelor’s or Master’s in a quantitative field (Statistics, Computer Science, Applied Mathematics, Economics)

Bonus / Nice to Have

• Deep learning frameworks: PyTorch, TensorFlow
• Cloud platforms: AWS, GCP
• Distributed computing: Spark, Databricks