Identifies business trends and problems through complex data analysis
Interprets results from multiple sources using a variety of techniques
Designs, develops, and implements high-impact, scalable business solutions
Partners cross-functionally to translate business needs into analytical frameworks and actionable insights
Leverage expertise in handling large, complex datasets to perform exploratory data analysis, feature engineering, and statistically sound sample design
Build, validate, deploy, and enhance complex predictive and machine learning models
Design, build, deploy, and monitor machine learning models in production environments
Implement model lifecycle management best practices
Automate feedback loops for algorithms and models in production
Design and execute experiments to evaluate model performance and business impact
Act as a technical subject matter expert and mentor junior data scientists
Requirements
Bachelor’s degree or equivalent experience in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Economics, or related discipline
Master’s or PhD preferred
5–7+ years of professional experience building, validating, deploying, and monitoring predictive and machine learning models in production environments
Strong programming expertise in Python with hands-on experience building end-to-end ML solutions
Experience with MLflow (or similar model lifecycle tools) for experiment tracking, model versioning, and deployment management
Experience working with SQL and large-scale data processing frameworks such as Apache Spark
Experience designing scalable data solutions in modern data platforms; experience with Microsoft Fabric is a plus