Quant Analytics Associate I – Fraud Strategy

JPMorgan Chase & Co.; Wilmington (DE); On-site; USD 80,000 - 100,000; Full time

Job summary

JPMorgan Chase & Co. is looking for a Quantitative Analytics Associate to join the Point of Sale Fraud team. In this role, you will handle fraud risk strategies and perform complex analyses to reduce fraud losses while enhancing customer experiences.

The ideal candidate will have a strong background in quantitative disciplines and experience in fraud or risk management, along with proficiency in Python, SAS, and SQL. Strong interpersonal communication skills are required to collaborate effectively with various teams and present findings to management.

Qualifications

• 2 or more years’ experience in fraud/risk/payments or related field.
• Ability to query large amounts of data.
• Experience delivering recommendations to management.

Responsibilities

• Interpret complex data to formulate conclusions regarding risk dynamics.
• Manage and implement fraud strategies to protect the bank.
• Identify key risk indicators and develop metrics.

Skills

Python

SAS

SQL

Analytical skills

Problem-solving

Communication skills

Education

Bachelor's degree in a quantitative discipline

Master's degree in a quantitative discipline

Job description

Drive impactful fraud prevention as a Quantitative Analytics Associate on our Point of Sale Fraud team—where your advanced risk analyses and strategic insights help reduce fraud losses, protect customers, and influence key decisions across the organization.

As a Quantitative Analytics Associate in the Point of Sale Fraud team, you will manage fraud risk strategies in the Fraud Policy area and perform complex risk analyses with the objective of reducing fraud related losses while balancing customer impact. You will frequently interact and communicate with cross-functional partners and communicate and present presentations to managers and executives.

Job Responsibilities

• Interpret large amounts of complex data to formulate problem statement, concise conclusions regarding underlying risk dynamics, trends, and opportunities
• Manage, develop, communicate, and implement optimal fraud strategies (including rules, cutoffs, policies, operational flows, etc.) to protect the bank from fraud related losses and improve customer experience at Point of Sale
• Identify key risk indicators and metrics, develop key metrics, enhance reporting, and identify new areas of analytic focus to better capture fraud.
• Provide subject matter expertise on strategy implementation/testing and initiatives related to the improvement of risk mitigation processes and infrastructure
• Collaborate with cross-functional partners to understand and address key business challenges
• Identify business opportunity by performing well thought analysis – Data mining, ensuring data integrity, synthesizing and communicating findings to senior management
• Assist team efforts in the critical development of new fraud pattern or spending pattern detection tools while providing clear/concise oral and written communication across various functions and levels, inclusive of Operations, IT, and Risk Management

Required Qualifications, Capabilities, and Skills

• Bachelor's degree (or related work experience) in a quantitative discipline in a financial services organization and 2 or more years’ experience in fraud/risk/payments or related field.
• Advanced understanding of Python, SAS, and SQL.
• Ability to query large amounts of data and transform raw data into actionable management information.
• Strong analytical and problem-solving abilities.
• Experience delivering recommendations to management.
• Self-starter with the ability to drive for resolution.
• Strong communication and interpersonal skills with the ability to interact with individuals across departments/functions and with senior-level executives.

Preferred Qualifications, Capabilities, and Skills

• Master's degree (or related work experience) in a quantitative discipline, preferably in a financial services organization, plus 2 or more years’ experience in fraud/risk/payments or related field.
• Experience with Machine Learning technologies and knowledge of LLMs.