Data Scientist Director

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Consumer & Community Banking

Director-level role focused on building and scaling a Center of Excellence for delivering AI/ML solutions at enterprise scale within a financial services context. The role emphasizes end-to-end productization, governance, and adoption of AI/ML capabilities, with a focus on measurable business outcomes and responsible AI practices.

What you'd actually do

  1. Define and continuously improve the Data and AI Center of Excellence operating model, including service catalog, delivery standards, reusable assets, and engagement approach
  2. Establish portfolio intake, prioritization, and sequencing with transparent criteria (value, feasibility, risk, time-to-impact, reusability)
  3. Lead end-to-end AI/ML productization, including data readiness, development, validation readiness, deployment into workflows, and ongoing monitoring
  4. Drive adoption by partnering with stakeholders to embed solutions into business processes and decision journeys
  5. Build a benefits realization approach with clear KPIs, measurement methods, and realized value tracking across revenue, cost, risk, and controls

Skills

Required

  • director-level leadership in data and analytics, AI/ML, decision science, or a related discipline within a matrixed enterprise environment
  • Proven end-to-end delivery of production AI/ML solutions, including deployment, monitoring, and sustained adoption
  • Demonstrated ability to quantify impact through KPI definition, attribution/measurement approach, and realized outcomes tracking
  • Proven ability to lead large, multi-disciplinary teams across geographies and influence outcomes without direct authority
  • Working knowledge of governance and controls relevant to AI/ML delivery (model risk concepts, privacy, security, change control, documentation expectations)
  • Strong executive communication skills, including the ability to drive alignment and decisions with senior stakeholders

Nice to have

  • Experience building, scaling, or operating a Data and AI Center of Excellence (or comparable shared delivery model)
  • Domain exposure adjacent to connected commerce (e.g., digital commerce, payments, customer engagement, marketing decisioning, platform products, operational decisioning)
  • Experience delivering across multiple markets with differing regulatory and data expectations
  • Familiarity with production observability and data publishing practices (release management, monitoring, incident response, retraining triggers)

What the JD emphasized

  • end-to-end execution
  • responsible AI practices
  • production AI/ML solutions
  • governance
  • model risk readiness
  • audit-ready

Other signals

  • enterprise scale
  • AI/ML capabilities
  • production AI/ML solutions
  • responsible innovation
  • responsible AI practices