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 products that directly influence senior decision-making. You will partner closely with leaders and stakeholders to turn ambiguous questions into measurable business outcomes.
Job summary
As an Executive Director, Data Science in the Chase 360 team within the consumer risk organization, you will lead a team of data scientists and analytics professionals to deliver insights and decisioning capabilities that materially improve credit and fraud risk outcomes. You will shape and execute a roadmap for durable (“evergreen”) data assets and scalable analytics products, from discovery through production. You will work across functions to identify the most important business problems, define success metrics, and translate opportunities into actionable analytical solutions. You will communicate clearly and credibly with both technical teams and senior leaders, balancing tradeoffs to drive timely decisions.
You will be expected to challenge existing paradigms, raise quality and explainability standards, and improve how data becomes insight at scale. The work spans structured and unstructured data, risk monitoring, and enterprise data product thinking—so you can move from strategy to execution while keeping teams aligned and motivated. You will also identify where generative artificial intelligence can responsibly accelerate the analytics lifecycle and improve analyst productivity.
Job responsibilities
- Lead and develop a high-performing data science team through clear direction, coaching, and a culture of high standards, curiosity, and continuous learning
- Define and execute a multi-year vision and roadmap for evergreen data assets, decisioning capabilities, and scalable analytics products that improve credit and fraud risk performance
- Expand the coverage, quality, and utility of key 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
- Lead generative artificial intelligence–enabled innovation across the data science lifecycle (for example, weak-signal discovery, entity and merchant enrichment, sequence understanding, and unstructured-to-structured transformation)
- 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
- Drive execution in a fast-paced environment by aligning stakeholders, prioritizing work across initiatives, and delivering against roadmap milestones
Required qualifications, capabilities, and skills
- 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
- Strategic and commercial mindset: 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
Preferred qualifications, capabilities, and skills
- 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