Vice President, Risk Analytics

JPMorgan Chase & Co.•Columbus, OH

1d•$171,000 - $245,000

About The Position

Join a team where advanced analytics, strong data foundations, and practical decisioning come together to improve outcomes for customers and the firm. You will lead high-impact data science initiatives that strengthen credit and fraud risk performance through better data, better models, and better insights. This role offers the opportunity to set a multi-year vision, build new capabilities, and scale analytics products that directly influence senior decision-making. You will partner closely with leaders and stakeholders to turn ambiguous questions into measurable business outcomes. As a Vice President, Risk Analytics in the Consumer Risk Insights team, you will lead a team of data scientists and analytics professionals to build and scale customer data assets and models that improve credit and fraud risk outcomes. You will work with partners across risk, product, technology, and operations to identify the most important problems, define success metrics, and translate opportunities into measurable analytical solutions. You will raise standards for quality and explainability so decision-makers can act with confidence. You will communicate clearly with both technical teams and senior leaders, using crisp storytelling to explain tradeoffs, recommendations, and expected impact. You will also identify where generative artificial intelligence can be used responsibly to accelerate the analytics lifecycle and improve productivity.

Requirements

Master’s degree in a quantitative field (for example, computer science, statistics, mathematics, physics, or related discipline)
Proven experience leading and developing data science teams, including coaching, performance management, and team culture
Demonstrated ability to deliver production-grade, data-driven solutions to complex business problems
Strong expertise in consumer financial services and applying analytics to risk decisioning, including credit and fraud
Deep knowledge of statistical modeling and data mining methods across structured and unstructured data
Strong programming capability in Python and SQL (and/or comparable analytics languages) and experience working with large-scale data
Ability to frame ambiguous problems, define clear success metrics, and prioritize high-impact work
Strong stakeholder management skills and ability to influence senior leaders through sound judgment and crisp storytelling
Excellent written and verbal communication skills for technical and non-technical audiences

Nice To Haves

Doctoral degree in a quantitative field
Experience building and scaling analytics “data products” used by multiple teams or functions
Hands-on experience applying generative artificial intelligence techniques to analytics workflows (for example, enrichment, classification, or unstructured text processing)
Experience improving transaction data quality, categorization, and explainability for downstream analytics or decisioning
Experience partnering with model risk management, governance, and control functions to support responsible deployment
Track record of delivering executive-level narratives and decision materials tied to measurable outcomes

Responsibilities

Lead and develop a high-performing data science team through clear direction, coaching, and a culture of high standards and continuous learning
Set and execute a multi-year roadmap for evergreen data assets, decisioning capabilities, and scalable analytics products that improve credit and fraud risk performance
Expand the coverage, quality, and usefulness of key customer data assets (for example, income-related data) to support risk decisions and insights
Advance data mining and modeling across structured and unstructured data to identify actionable insights and early indicators of consumer and small business stress
Reimagine end-to-end transaction categorization to improve quality, explainability, scalability, and speed to availability for downstream risk use cases
Partner with cross-functional stakeholders and risk leadership to prioritize opportunities, define success metrics, and translate business questions into analytical solutions
Deliver executive-ready narratives that clearly communicate insights, recommendations, tradeoffs, and expected business impact