Business Analysis Associate I

Join our Fraud Strategy team play a key role in protecting clients developing data-driven fraud detection solutions

Join our Fraud Strategy and Analytics team and play a key role in protecting clients by developing data-driven fraud detection solutions. Be part of the Commercial and Investment Banking Payments Operations team, the largest global cash management provider, striving for service excellence through client focus, technology solutions, and workforce innovation. This role offers exposure to fraud strategy, advanced analytics, machine learning, and executive-level stakeholder engagement, providing a strong foundation for career growth within Fraud Strategy and Payments Operations.

As a Business Analysis Associate I within JPMorgan Chase, you will play a key role in driving fraud prevention strategies. You will leverage your advanced understanding of data analytics to uncover patterns, analyze complex data sets, and propose innovative solutions to improve fraud detection. Your expertise in cross-functional collaboration will enable you to work effectively with diverse teams, ensuring alignment with business strategies.

You will also utilize your advanced computer literacy and digital proficiency to enhance processes and customer interactions. Your results will have a significant impact on the team and departmental goals, contributing to the overall success of the firm.

Job responsibilities

Support day-to-day responsibilities related to data analytics and delivery of enhanced fraud detection controls

Analyze and interpret complex data sets using advanced data analytics skills to support fraud prevention initiatives and strategic decision-making

Perform data analytics, data mining, predictive analytics

Deliver actionable reporting and insights that support strategic fraud risk decisions and executive-level discussions

Mitigate risk through continuous process improvement and root cause analysis

Drive business requirements and collaborate with Technology and Operations teams to implement new fraud prevention capabilities

Maintain a robust governance, risk, and control environment

Required qualifications, capabilities, and skills

Undergraduate or Master's degree in Analytics, Data Science, Mathematics / Statistics, Finance, Business, Computer Science, or related quantitative field

Demonstrated ability to conduct data analysis and interpret complex data sets, with at least 4 years of relevant work experience

Self-motivated and driven to deliver accurate results, critically think through solutions, and run business critical processes

Advanced Microsoft Excel skills; Proficient with other Microsoft Applications (PowerPoint, Visio, Word, etc.)

Proficiency querying enterprise databases using SQL is a must for this position. Knowledge in Python or other programming languages is a plus

Experience with Tableau, Alteryx, Qlik/QlikSense or other BI tools to perform statistical analysis

Ability to craft clear and effective prompt writing to guide data analysis and ensure consistent outcomes by leveraging Large Language Models (LLMs)

Advanced proficiency in using automation technologies to optimize processes and implement business strategies

Strong interpersonal skills and the ability to work collaboratively

Preferred qualifications, capabilities, and skills

Experience in fraud detection / anomaly detection and rule writing

Knowledge of Core Cash payment products including ACH, Wire, and RTP, with understanding of end-to-end transaction flows and associated fraud risks and controls

Proficiency with Amazon Web Service (AWS) cloud environment

Experience in technology / change management, with proficiency in using software applications, digital platforms, and other tools to solve problems and improve processes; Proficiency with JIRA is a plus

Aptitude and curiosity to leverage AI, machine learning, Large Language Models (LLMs), and automation technologies to strengthen data analysis, analyze business trends, identify business opportunities, and enhance decision-making processes