Data Scientist

About the role

The role owns the end-to-end data science lifecycle, building predictive models and statistical frameworks that drive product decisions and business growth. You will collaborate closely with data engineers, product managers, and software teams to translate complex business problems into rigorous, scalable machine learning solutions.

Responsibilities

• Develop, validate, and deploy statistical and machine learning models for classification, forecasting, and recommendation systems
• Write production-grade Python code to build robust data transformation and feature engineering pipelines using Pandas, NumPy, and SQL
• Design and analyze rigorous A/B experiments to measure the impact of new product features and algorithms
• Collaborate with data engineering teams to optimize data storage, ingestion, and feature stores for low-latency model inference
• Monitor deployed models for performance degradation, concept drift, and data quality issues, implementing retraining strategies as needed
• Communicate complex technical findings and model performance metrics clearly to cross-functional stakeholders and leadership

Requirements

• 3–6 years of professional experience in data science, applied statistics, or quantitative machine learning
• Advanced proficiency in Python and SQL with strong fundamentals in software engineering best practices and version control
• Solid understanding of statistical inference, hypothesis testing, experimental design, and predictive modeling techniques
• Experience deploying models into production using cloud platforms such as AWS, GCP, or Azure
• MS or PhD in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field
• Bonus: Experience with LLMs, time-series forecasting, or distributed computing frameworks like Spark