Data Scientist

Role overview

Design, build, and scale high-performance machine learning models and data pipelines that power core product decisions and automated user experiences.

Responsibilities

• Design, train, and validate predictive models for production using Python, scikit-learn, and PyTorch
• Build and maintain robust ETL and feature engineering pipelines for terabytes of structured and unstructured data using SQL and PySpark
• Deploy ML models to cloud infrastructure using Docker and orchestration on AWS or GCP
• Monitor deployed models for performance degradation, data drift, and latency bottlenecks, and implement automated remediation
• Partner with product and engineering teams to translate business needs into quantitative specifications and milestones

Requirements

• 3–6 years of professional experience in data science, quantitative analytics, or machine learning engineering
• Strong proficiency in Python, SQL, and scientific computing libraries including Pandas, NumPy, and Scikit-Learn
• Experience deploying and maintaining ML models in production cloud environments (AWS, GCP, or Azure)
• Solid understanding of statistical modeling, experimental design, A/B testing, and hypothesis testing

Nice to have

• Master’s or Ph.D. in CS, Statistics, Mathematics, or a related field
• Contributions to open-source data science libraries