Quant Analytics

What you'll do

• Partner with leaders to frame business problems, define success metrics, and translate objectives into analytical approaches.
• Acquire, clean, and integrate structured and unstructured data from multiple internal sources into repeatable pipelines.
• Build, validate, and iterate predictive and prescriptive models such as classification, regression, and forecasting.
• Design and analyze experiments using A/B testing and quasi-experimental methods to communicate causal insights.
• Develop features, evaluate model performance, and implement monitoring for drift, bias, and operational stability.
• Operationalize analytics by collaborating with engineering teams to deploy models into production environments using Databricks.
• Create decision-oriented storytelling artifacts, including executive readouts and metric definitions, to ensure insight adoption.
• Strengthen analytics governance by applying best practices for reproducibility, documentation, and model risk management.

What we're looking for

• Bachelor's degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field.
• Master's or PhD in a quantitative field (preferred).
• 2 years of relevant experience delivering end-to-end analytics or machine learning solutions.
• Strong proficiency in Python and/or R for data analysis and modeling.
• Experience with SQL and working with relational and/or distributed data platforms.
• Experience with Databricks, including notebooks, workflows, Unity Catalog, or similar platform features.
• Solid foundation in statistics, probability, and machine learning, including model evaluation and validation practices.
• Familiarity with software engineering practices including version control, code review, testing, and reusable component design.
• Ability to communicate technical concepts clearly, including model behavior, uncertainty, and practical limitations.
• Experience deploying and monitoring models in production, including MLOps patterns (preferred).
• Experience with cloud analytics stacks and modern data tooling (preferred).
• Familiarity with responsible AI practices, including fairness assessment and explainability (preferred).
• Domain experience aligned to the hiring organization's products, operations, or risk context (preferred).