How you will make an impact

• Model Ownership: Participate in the full modeling lifecycle, from statistical analysis and experimentation to building, validating, and iterating on machine learning models that address critical business challenges

• Data Quality: Own the data foundation by preparing, cleaning and transforming raw, complex data into high-quality features for modeling. Proactively identify and handle missing values, outliers, and inconsistencies

• Problem Investigation: Investigate data discrepancies (tracking bugs, ETL errors, definitional issues) and design automated frameworks to ensure data accuracy

• Cross-Functional Collaboration: Act as a strategic liaison, collaborating with data Engineering and product teams to drive the data strategy and definition of our centralized feature store, ensuring it becomes the 'single source of truth' for all ML models

• Documentation: Create and maintain clear, authoritative documentation for data sources, cleaning processes, and variable definitions

Requirements

• Education: Bachelor’s degree (PhD preferred) in a quantitative field (Statistics, Physics, Mathematics, etc.)

• Programming: Strong proficiency in Python (Pandas/NumPy) and SQL for complex querying and data manipulation

• Data Quality: Hands-on experience with data cleaning techniques and data validation frameworks

• Tooling: Familiarity with data visualization tools to help identify and communicate data issues