Data Scientist - Process Intelligence Analytics

JPMorganChase; Plano (TX); On-site; USD 95,000 - 130,000; Full time

Job summary

JPMorgan Chase is seeking a Data Scientist to translate business questions into rigorous analytical studies in an onsite role in Plano, TX. You will use Python, SQL, and statistical/machine learning methods to connect data with operational context and deliver evidence-based recommendations for decision-making.

Responsibilities include clarifying questions with stakeholders, preparing data from multiple sources, applying ML, and presenting actionable insights with uncertainty analysis, while

Qualifications

• Bachelor's degree in a quantitative field with strong methodological training

Responsibilities

• Translate business questions into analytical studies and plan data sources
• Evaluate data quality and limitations, justify methods
• Prepare and integrate data from various sources
• Apply statistical methods and ML to produce reproducible analyses
• Present findings with clear recommendations and uncertainty considerations

Skills

Python

SQL

Pandas

Scikit-learn

Data visualization

Education

Bachelor's degree in Data Science/CS/Statistics

Tools

Tableau

Power BI

Snowflake

Spark

AWS

Job description

The Process Intelligence Analytics team is seeking a Data Scientist to translate business questions into rigorous analytical studies. In this onsite role in Plano, TX, you will use Python, SQL, and statistical and machine learning methods to connect data with operational context and deliver evidence-based recommendations for decision-making.

Key Responsibilities

• Work with stakeholders and senior team members to clarify the business decision, question, and underlying assumptions before selecting data sources or analytical methods.
• Evaluate data relevance, quality, and limitations, and recommend additional evidence or alternative methods when required.
• Prepare and integrate data from task mining, process mining, operational records, surveys, and interviews, using approved access controls.
• Apply statistical methods and machine learning where appropriate, producing reproducible analyses aligned to the question.
• Detect variation, workarounds, rework, and overlooked effort, and test competing explanations.
• Investigate unexpected patterns and iteratively refine questions as new findings emerge.
• Avoid interpreting activity data as a direct measure of effort or performance, and avoid treating associations as causal claims.
• Present findings as clear recommendations tied to decision options, including uncertainty and next-step actions.
• Analyze work systems at an aggregate level, in line with privacy and responsible‑use requirements, rather than assessing individual employees.

Required Qualifications

• 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 convert an ambiguous business question into an analysis plan and justify data and method selections.
• Strong judgment in evaluating evidence quality, testing assumptions, and communicating uncertainty.
• Proficiency with Python, SQL, pandas, and scikit-learn.
• Knowledge of statistical methods such as regression, including assumptions and limitations.
• Clear communication and collaboration with stakeholders across disciplines.

Preferred Qualifications

• Master's degree in a relevant field.
• Exposure to task mining, process mining, or workflow analysis.
• Experience designing or analyzing experiments or evaluations of operational changes.
• Experience combining quantitative outputs with qualitative data (for example, interviews or surveys), including familiarity with measurement validity, psychometrics, human factors, or organizational research.
• Experience developing AI-powered analytics, workflow automation, or intelligent process tools to improve operational efficiency, reduce manual effort, or enhance accuracy.
• Familiarity with Tableau or Power BI and cloud data tools such as Snowflake, Spark, or AWS.

Technologies

• Python, SQL, pandas, scikit-learn
• Tableau, Power BI
• Snowflake, Spark, AWS

Team Overview

Our Consumer & Community Banking division serves Chase customers through financial services including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans, and payment processing. Operations teams create and manage innovative, secure service solutions globally to deliver industry-leading capabilities and refine technology-driven customer and client experiences.