Data Scientist Associate

The Internal Audit Department provides the Board of Directors, senior management and regulators with an independent assessment of JPMorgan Chase's ('JPMC') control environment. Audit works closely with the Lines of Business ('LOBs') and their support functions to achieve its mission through the execution of a comprehensive audit program designed to test the effectiveness of the controls in place to mitigate the risks inherent in each business. The department is respected throughout the firm for the caliber of the staff and their ability to add value beyond the audit opinion.

As a Data Analytics Solutions Associate within Consumer and Community Banking, you will partner closely with Internal Audit teams as well as peers to design relevant audit testing that leverages data. This includes strong interpersonal skills to clarify requests, help shape and identify requirements, and continually report on progress. In addition, you will develop and leverage skills for sourcing, cleansing and staging data, developing data analytic solutions, and supporting/developing data science initiatives. You will work with peers and audit stakeholders to gain a deep understanding of the data and architecture in order to deliver relevant solutions and results.

The role core tools, technologies and techniques for this role are as follows: SQL, Python (pandas, numpy, visualization libraries), Alteryx, workflow enablement/agentic tooling; Agentic Studio, Smart SDK, AI Code Assistance tools (Claude Code, GitHub Copilot), data preparation, validation and cleansing, cloud data platforms; Databricks or Snowflake, and Visualization Analytics (Tableau, or similar).

Job Responsibilities

• Deliver end-to-end analytics and data science solutions across the audit lifecycle-from problem framing and requirements through data acquisition, analysis/modeling, visualization, and deployment-using tools including SQL, Python, Alteryx, Databricks, Tableau, Agentic Studio, Smart SDK, and related platforms.
• Translate audit objectives into clear analytic hypotheses and test designs, selecting appropriate methods (descriptive, diagnostic, predictive, and anomaly detection) to support risk-based audit scoping and execution.
• Partner closely with audit leads and key stakeholders to shape and refine the analytics and data science requirements; proactively manage relationships, expectations, scope changes, and communications to drive value and efficiency based results.
• Engineer repeatable, scalable analytics and data science based solutions (datasets, reusable code modules, workflows, dashboards, and templates) that improve efficiency and enable auditor self-service where appropriate.
• Design and develop solutions for non - audit cycle based activities, including continuous auditing, continuous monitoring, automated testing, advanced testing, and event/trigger-based analytics to identify emerging risks.
• Apply strong data management and governance practices-including data lineage, data quality assessment, access controls, documentation, and definitions/metadata-to ensure analytics are reliable, auditable, and reproducible.
• Implement quality controls for analytic outputs, including validation checks, reasonableness testing, peer review, and clear documentation of assumptions, limitations, and interpretability (especially for advanced models).
• Manage multiple concurrent deliverables by planning work, prioritizing effectively, and meeting timelines and budget expectations while maintaining high standards for accuracy and usability.
• Continuously evaluate and adopt new tools/techniques to improve team effectiveness; recommend enhancements to processes, automation opportunities, and platform capabilities.
• Communicate insights clearly to varied audiences (audit teams, technology/data partners, and senior stakeholders), tailoring messaging and visuals to drive understanding and action.
• Contribute to team knowledge-sharing by providing perspectives on where analytics/data science can add value, and by supporting enablement through guidance, demos, and lightweight training.

• Bachelor's degree in Computer Science, Data Analytics, Data Science, Information Systems, Engineering, or a related discipline (or equivalent practical experience).
• 3+ years of experience in Audit, Data Analytics, Data Science, Risk/Controls, or a closely related role.
• Demonstrated experience working with large, complex datasets (multiple disparate sources, high volume), performing data wrangling, validation, enrichment and building analytics and data science based solutions..
• Proven, recent track record of building and delivering repeatable, production-ready data science and analytical solutions (e.g., automated workflows, dashboards, anomaly detection, model development)
• Strong understanding of data ecosystems (databases, data warehouses/lakes, ETL/ELT patterns, APIs/files), and how technology design influences risk, controls, and auditability.
• Working knowledge of technology and data risks/controls and the ability to apply this experience when designing solutions.
• Excellent written and verbal communication with the ability to explain technical concepts to non-technical audiences; strong interpersonal skills to build partnerships and influence outcomes.
• Strong critical thinking and structured problem-solving skills-able to frame ambiguous questions, test hypotheses, and identify practical solutions under time constraints.
• Ability to manage and deliver multiple concurrent tasks with attention to detail, effective prioritization, and follow-through against timelines.
• Working knowledge of data management principles such as data quality, lineage, metadata, governance, privacy/access considerations, and documentation practices that support reproducibility.
• Self-motivated, proactive; demonstrates accountability, sound judgment, and the ability to operate through ambiguity while maintaining high standards. Strong professionalism and integrity; able to work with limited supervision.