Quant Analytics Associate at JPMC Candidate Experience page

Mumbai, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

27 May, 26

Salary

0.0

Posted On

26 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SAS, SQL, Data Analysis, Problem Solving, Strategy Development, Risk Indicators, Metric Development, Reporting Enhancement, Large Language Models, Machine Learning, Natural Language Processing, AWS, Snowflake, Communication, Data Transformation

Industry

Financial Services

Description

Join our dynamic team as a Quantitative Analytics Associate in the Consumer and Community Banking Fraud Prevention Optimization Strategy team. You'll play a key role in reducing fraud costs and enhancing customer experience through complex analyses and collaboration. This position offers growth opportunities and the chance to work with cross-functional partners, presenting insights to managers and executives. Be part of a team that leverages advanced analytics and innovative tools to promote impactful business improvements. Job Summary As a member of the Fraud Prevention Optimization team, you will focus on reducing cost of fraud, through complex analyses combined with business insights and collaboration. Our objective is reducing losses and / or operating expenses while balancing customer impact through optimizing business processes and decisioning. Team members will frequently interact and communicate with cross-functional partners and present complex analysis succinctly to managers and executives. This role provides an opportunity to be part of a dynamic team that is instrumental in protecting the bank by leveraging complex analytics and new tools like large language models to deliver sustainable, hard hitting business improvements. Job Responsibilities Interpret and analyze complex data to formulate problem statement, provide concise conclusions regarding underlying risk dynamics, trends, and opportunities. Use advanced analytical & mathematical techniques to solve complex business problems. Manage, develop, communicate, and implement optimal fraud strategies to reduce fraud related losses and improve customer experience across credit card fraud lifecycle. Identify key risk indicators, develop key metrics, enhance reporting, and identify new areas of analytic focus to constantly challenge current business practices. Provide key data insights and performance to business partners. Collaborate with cross-functional partners to solve key business challenges. Assist team efforts in the critical projects while providing clear/concise oral and written communication across various functions and levels. Champion the usage of latest technology and tools, such as large language models, to drive value at scale across business organizations. Required qualifications, capabilities, and skills Bachelor’s degree in a quantitative field or 3 years risk management or other quantitative experience Background in Engineering, statistics, mathematics, or another quantitative field Advanced understanding of Python, SAS, and SQL Query large amounts of data and transform into actionable recommendations. Strong analytical and problem-solving abilities Experience delivering recommendations to leadership. Self-starter with ability to execute quickly and effectively. Strong communication and interpersonal skills with ability to interact with individuals across departments/functions and with senior level executives Preferred qualifications, capabilities, and skills MS degree in a quantitative field or 2 or more years risk management or other quantitative experience. Hands on Knowledge of AWS and Snowflake. Advanced analytical techniques like Machine Learning, Large Language Model Prompting or Natural Language Processing will be an added advantage. JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

Responsibilities

The role involves interpreting and analyzing complex data to formulate problem statements, draw conclusions on risk dynamics, and manage the development and implementation of optimal fraud strategies to reduce losses and improve customer experience. Team members will frequently interact with cross-functional partners and present complex analysis succinctly to management.