Quant Analytics Senior Associate - Credit Card Installment

Join a team where your analytical expertise directly shapes pricing decisions that improve customer outcomes and business performance. You will work on high-impact initiatives, apply rigorous quantitative methods, and partner closely with teams across the organization to turn insights into action. If you enjoy solving ambiguous problems and delivering measurable results, this role offers strong opportunities to grow your technical depth and influence.

As a Quant Analytics Senior Associate at JPMorganChase within the Credit Card Installment team, you will deliver quantitative research and pricing analytics that inform strategy, experimentation, and performance measurement. You will apply advanced statistical techniques and strong business judgment to evaluate pricing approaches, understand customer behavior, and improve policies and configuration. You will collaborate with cross-functional partners to translate findings into decisions and execution.

Job responsibilities

• Deliver effective quantitative problem solving and analytical research to support key pricing initiatives

• Apply deep business understanding and advanced analytical techniques to drive research, experimentation, and measurable outcomes

• Lead, mentor, hire, and develop a high-performing analytics team; promote scientific rigor, ethical artificial intelligence, and continuous learning

• Maintain a pragmatic approach to generative artificial intelligence and large language models as complementary tools, while prioritizing classical, statistical, and graph analytical methods for core pricing strategy workstreams

• Partner with cross-functional teams (for example, Finance and Marketing) to drive insights into action and deliver business impact

• Design and implement scalable, reliable analytics processes to optimize business outcomes

• Conduct extensive analysis of pricing functionality, configuration, and customer behavior to improve pricing strategies and policies using advanced quantitative methods

• Own and support strategic pricing initiatives, including effectiveness evaluation and profit and loss analysis; quantify statistical and practical significance

• Solve unstructured business problems and develop deep-dive analyses of customer behavior using multiple analytics and statistical techniques

• Conduct hypothesis testing and advanced experimental design (A/B and multivariate tests) to measure the impact and effectiveness of pricing strategies

Required qualifications, capabilities and skills

• Graduate or post-graduate degree in a quantitative discipline (for example, Computer Science, Statistics, Mathematics, Finance, Economics, Data Analytics, or Machine Learning)

• 3+ years of hands-on analytics experience in banking strategic analytics

• Hands-on proficiency with Python and SQL; experience with visualization tools (for example, Tableau) and analytics tools (for example, Alteryx)

• Strong statistical and econometric foundation with hands-on pricing analytics experience, including elasticity estimation, A/B testing, causal inference, and experimentation frameworks

• Strong advanced analytics skills using SAS, Python, or R

• Excellent communication skills with the ability to translate complex models into clear explanations and “reason codes,” influencing cross-functional stakeholders and senior leadership

• Exposure to enterprise artificial intelligence enablement, large language model-assisted workflows, or analytics transformation programs

• Ability to evaluate opportunities to apply artificial intelligence, generative artificial intelligence, and intelligent automation to improve investigative analysis, control documentation, operating procedures, knowledge retrieval, issue summarization, and workflow efficiency

• Familiarity with supervised learning, anomaly detection, semi-supervised learning, clustering, feature stores, calibration and threshold optimization, imbalanced learning, and pricing sensitivity evaluation

Preferred qualifications, capabilities and skills

• Proficiency in extract, transform, load processes across structured and unstructured data sources

• Professional experience with Amazon Web Services, Apache Spark, Amazon EMR, Snowflake, Confluence, and generative artificial intelligence tools (subject to firm-approved usage)

• People leadership experience, including recruiting, coaching, performance management, and fostering an inclusive, high-accountability culture

• Strong understanding of information technology processes and databases, with the ability to work directly with data owners and custodians