Quant Analytics

JPMorgan Chase & Co.•Columbus, OH

17h

About The Position

Embark on a rewarding and challenging career with our dynamic team. This is a corporate technology environment with cross-functional collaboration across product, engineering, and business teams. The Sr. Associate, Data Scientist will be embedded within the Data and Analysis team and will engage regularly with stakeholders across the organization. The role typically involves a mix of independent deep-work (analysis, modeling, experimentation) and structured stakeholder engagement (requirements, readouts, decision support). The Sr. Associate, Data Scientist is expected to operate with strong data stewardship, adherence to internal controls, and documentation standards consistent with enterprise change management and risk expectations. As a Quant Analytics within JPMorganChase, you will develop and deploy analytical solutions that improve decision-making, operational performance, and customer outcomes across the enterprise as a member of the Data and Analysis team. This role is suited for a data scientist who works with meaningful autonomy on moderately complex problems and is actively developing toward senior-level expertise. You will combine statistical modeling, machine learning, and strong business partnership to translate ambiguous questions into measurable results. You will contribute to the team's Databricks-based analytical environment and is expected to demonstrate rigorous experimentation, disciplined engineering practices, and clear communication with both technical and non-technical stakeholders.

Requirements

Strong proficiency in Python (and/or R) for data analysis and modeling.
Solid foundation in statistics, probability, and machine learning, including model evaluation and validation practices.
Experience with SQL and working with relational and/or distributed data platforms.
Experience with Databricks, including notebooks, workflows, Unity Catalog, or similar platform features.
Familiarity with software engineering practices: version control (for example, Git), code review, testing, and reusable component design.
Ability to communicate technical concepts clearly, including model behavior, uncertainty, and practical limitations.
2 years of relevant experience delivering end-to-end analytics or machine learning solutions, from problem definition through deployment and measurement. Candidates should be able to articulate tradeoffs, assumptions, and impact in prior work. Hands-on experience with Databricks (notebooks, workflows, Unity Catalog, or similar platform features) is required, as it is the team's primary analytical platform.
Bachelor's degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field.

Nice To Haves

Master's or PhD in a quantitative field.
Experience deploying and monitoring models in production, including MLOps patterns (CI/CD, model registries, automated retraining, observability).
Experience with cloud analytics stacks and modern data tooling, including platforms such as Databricks for unified analytics, data lakes, and ML workflows.
Familiarity with responsible AI practices, including fairness assessment, explainability methods, and governance documentation.
Domain experience aligned to the hiring organization's products, operations, or risk context.

Responsibilities

Partner with business and technology leaders to frame problems, define success metrics, and translate objectives into analytical approaches.
Acquire, clean, and integrate structured and unstructured data from multiple internal sources; implement repeatable data preparation pipelines.
Build, validate, and iterate predictive and prescriptive models (for example: classification, regression, forecasting, anomaly detection, ranking, and optimization) aligned to business needs.
Design and analyze experiments (A/B testing, quasi-experimental methods) and communicate causal insights, limitations, and recommended actions.
Develop features, evaluate model performance, and implement monitoring for drift, bias, and operational stability.
Operationalize analytics by collaborating with data engineering and application teams to deploy models and data products into production environments, including within the team's Databricks-based environment.
Create clear, decision-oriented storytelling artifacts (executive readouts, metric definitions, and documentation) to ensure insights are understood and adopted.
Strengthen analytics governance by applying best practices for reproducibility, documentation, model risk management, and appropriate use of data.
Promote to team knowledge sharing by participating in peer review, documentation practices, and a culture of scientific rigor and continuous improvement.