Data and AI Control Manager

JPMorgan Chase JPMorgan Chase · Banking · Hyderabad, Telangana, India · Commercial & Investment Bank

This role focuses on designing and executing data-driven control testing within financial services, specifically for client onboarding and documentation. It involves building and productionizing AI/ML solutions, including Generative AI, for risk detection, continuous monitoring, and regulatory compliance. The role also emphasizes automating testing processes, operationalizing alerts, and partnering with various teams to ensure explainability and auditability, with a strong focus on regulated environments.

What you'd actually do

  1. Lead strategic control testing engagements using analytics to surface gaps and BAU breaks; drive actionable remediation.
  2. Build and productionize AI/ML solutions (classification, anomaly detection, risk scoring, entity resolution) and GenAI workflows.
  3. Automate end‑to‑end testing and control processes with Alteryx, Power BI/Tableau, and workflow/RPA platforms.
  4. Operationalize proactive alerts and dashboards for KRIs and regulatory priorities.
  5. Partner with WKO, DDS, Controls, Operations, Technology, and Data teams to define requirements and ensure explainability/auditability.

Skills

Required

  • Python or R for data manipulation, model development, and testing automation at scale
  • Modern ML, pattern recognition, and statistical analysis with clear understanding of limitations in regulated environments
  • Consume APIs and integrate diverse data sources while adhering to data governance, lineage, and quality standards
  • Execute control testing with strong control design, root cause analysis, and documentation discipline
  • Partner across business, operations, and technology to deliver measurable risk and control outcomes

Nice to have

  • Leverage hands‑on experience with LLMs/GenAI and ML techniques for predictive modeling and monitoring
  • Utilize advanced analytics (regression, classification, clustering, dimensionality reduction) to improve control effectiveness
  • Manage 2–3 automation initiatives concurrently across global teams and time zones with clear governance
  • Strengthen compliance by aligning evidence to audit and model risk standards and ensuring explainability
  • Enhance data reliability via metadata, lineage tracking, and secure usage aligned to regulatory requirements
  • Communicate complex findings through concise executive summaries, dashboards, and stakeholder forums
  • Institutionalize lessons learned via playbooks, standardized test scripts, and reusable control components

What the JD emphasized

  • regulated environments
  • regulatory compliance
  • audit and model risk governance
  • audit-ready evidence
  • explainability/auditability
  • model risk standards
  • regulatory requirements

Other signals

  • build and productionize AI/ML solutions
  • apply AI/ML—including Generative AI—to enhance risk detection
  • apply LLMs/GenAI for document parsing, policy mapping, and exception narrative synthesis using RAG and prompt engineering