Data Scientist – Process Intelligence Analytics

Join the Process Intelligence team at JPMorgan Chase to investigate how people, processes, and technology shape work. We start with the decision leaders need to make, then use relevant evidence to test assumptions and identify improvements that benefit employees, customers, and the business. As a Data Scientist at JPMorgan Chase, you will work with business partners, researchers, engineers, and senior team members to turn business questions into analytical studies. Using Python, SQL, and statistical methods, with machine learning where appropriate, you will combine data with operational context and communicate what the evidence supports and what remains uncertain. Job Responsibilities: Clarify the business decision, question, and assumptions with stakeholders and senior team members before selecting data or methods. Assess data relevance, quality, and limitations, and recommend additional evidence or methods when needed. Prepare and integrate data from task mining, process mining, operational records, surveys, and interviews, within approved access. Apply statistical methods, and machine learning where appropriate, in reproducible analyses suited to the question. Identify variation, workarounds, rework, and overlooked effort, and test competing explanations. Explore unexpected patterns and refine questions as findings emerge. Avoid treating activity data as a direct measure of effort or performance, or associations as causes. Communicate findings as clear recommendations tied to decision options, uncertainty, and next steps. Analyze work systems at an aggregate level, following privacy and responsible-use requirements, rather than rating individual employees. Required Qualifications, Capabilities, and Skills : Bachelor’s degree in Data Science, Computer Science, Statistics, Economics, Industrial-Organizational Psychology, Industrial Engineering, or a related field, with quantitative training such as econometrics or research methods. 1–3 years of experience in data science or applied research. Ability to turn an ambiguous business question into an analysis plan and justify the data and methods chosen. Sound judgment in assessing evidence quality, testing assumptions, and communicating uncertainty. Proficiency in Python, SQL, pandas, and scikit-learn. Knowledge of statistical methods such as regression, including their assumptions and limitations. Clear communication and collaboration with stakeholders across disciplines. Preferred Qualifications, Capabilities, and Skills: Master’s degree in one of the fields above. Exposure to task mining, process mining, or workflow analysis. Experience designing or analyzing experiments or other evaluations of operational changes. Experience combining quantitative findings with qualitative data, such as interviews or surveys, and familiarity with measurement validity, psychometrics, human factors, or organizational research. Developing experience using AI-powered analytics, workflow automation, or intelligent process tools to drive efficiency gains, reduce manual effort, or improve accuracy in operational processes. Familiarity with Tableau or Power BI and cloud data tools such as Snowflake, Spark, or AWS.