Data Scientist

About the role

The role owns the end-to-end data science lifecycle, turning complex datasets and business challenges into scalable predictive models and actionable insights. The team collaborates closely with data engineers and product stakeholders to ensure models deliver measurable impact while maintaining high standards of accuracy and reliability in production.

Responsibilities

• Develop and deploy machine learning models, statistical frameworks, and advanced analytics pipelines to solve core business challenges
• Perform exploratory data analysis and feature engineering using Python, SQL, and distributed computing frameworks like PySpark
• Collaborate with data engineering teams to structure, clean, and optimize data assets for training and inference environments
• Evaluate model performance rigorously, tracking metrics, conducting A/B tests, and iterating to improve predictive accuracy
• Communicate complex technical findings and data-driven insights clearly to cross-functional stakeholders and leadership
• Contribute to internal data science tooling, best practices, and code quality standards through peer reviews and documentation

What we are looking for

• 3–6 years of professional experience in data science, applied statistics, or quantitative machine learning
• Advanced proficiency in Python and SQL, with strong hands-on experience using libraries such as scikit-learn, pandas, NumPy, and PyTorch or TensorFlow
• Demonstrated experience deploying and monitoring machine learning models in production cloud environments such as AWS, GCP, or Azure
• Solid foundation in statistical modeling, hypothesis testing, experimental design, and machine learning fundamentals
• Bachelor's or Master's degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field
• Bonus: Experience with LLM integrations, MLOps tooling like MLflow, or publishing research in peer-reviewed venues