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Data and AI Control Manager

ExperiencedNo visa sponsorship
J.P. Morgan logo

at J.P. Morgan

Bulge Bracket Investment Banks

Posted 15 days ago

No clicks

**Data & AI Control Manager - Bengaluru, India** Design and lead strategic control testing, embracing data analytics, AI/ML, and automation (Alteryx, Power BI, workflow/RPA) to enhance risk detection and regulatory compliance across Client Onboarding & Documentation (WKO, DDS). Partner cross-functionally to close control gaps, drive actionable remediation, and maintain audit-ready evidence.

Compensation
Not specified

Currency: Not specified

City
Bengaluru
Country
India

Full Job Description

Location: Bengaluru, Karnataka, India

As a Data & AI Control Manager within the Strategic Control Testing group in OCM, you design and execute datadriven control testing across Client Onboarding & Documentation, including WKO and DDS. You build automation and apply AI/MLincluding Generative AIto enhance risk detection, continuous monitoring, and regulatory compliance. You partner across businesses and Corporate Functions to close control gaps, uplift resilience, and maintain auditready evidence.

Job Responsibilities 

  • Lead strategic control testing engagements using analytics to surface gaps and BAU breaks; drive actionable remediation.
  • Build and productionize AI/ML solutions (classification, anomaly detection, risk scoring, entity resolution) and GenAI workflows.
  • Automate endtoend testing and control processes with Alteryx, Power BI/Tableau, and workflow/RPA platforms.
  • Operationalize proactive alerts and dashboards for KRIs and regulatory priorities.
  • Partner with WKO, DDS, Controls, Operations, Technology, and Data teams to define requirements and ensure explainability/auditability.
  • Maintain highquality reporting and executive summaries on trends, systemic issues, and control weaknesses.
  • Stay current on LLMs/GenAI and advanced ML; identify highvalue use cases with appropriate guardrails.
  • Design statistical tests and MLbased monitors for policy, legal, and regulatory compliance aligned to audit and model risk governance.
  • Build pipelines to ingest, profile, cleanse, and join large datasets from systems of record for repeatable analytics.
  • Apply LLMs/GenAI for document parsing, policy mapping, and exception narrative synthesis using RAG and prompt engineering.
  • Translate KYC/CO&D requirements into measurable tests and rules; tune to reduce false positives and document methods for audits.

Required Qualifications, Capabilities, and Skills 

  • Hold a bachelors or advanced degree in a quantitative field (Computer Science, Statistics, Engineering, Data Science).
  • Bring 6+ years in risk management/financial services across compliance, financial crimes, operational risk, audit, or BPM with automation/modeling exposure.
  • Demonstrate proficiency in Python or R for data manipulation, model development, and testing automation at scale.
  • Apply 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.

Preferred Qualifications, Capabilities, and Skills 

  • Leverage handson 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 23 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.
Lead strategic control testing, embedding data, intelligence and automation.

Data and AI Control Manager

Compensation

Not specified

City: Bengaluru

Country: India

J.P. Morgan logo
Bulge Bracket Investment Banks

15 days ago

No clicks

at J.P. Morgan

ExperiencedNo visa sponsorship

**Data & AI Control Manager - Bengaluru, India** Design and lead strategic control testing, embracing data analytics, AI/ML, and automation (Alteryx, Power BI, workflow/RPA) to enhance risk detection and regulatory compliance across Client Onboarding & Documentation (WKO, DDS). Partner cross-functionally to close control gaps, drive actionable remediation, and maintain audit-ready evidence.

Full Job Description

Location: Bengaluru, Karnataka, India

As a Data & AI Control Manager within the Strategic Control Testing group in OCM, you design and execute datadriven control testing across Client Onboarding & Documentation, including WKO and DDS. You build automation and apply AI/MLincluding Generative AIto enhance risk detection, continuous monitoring, and regulatory compliance. You partner across businesses and Corporate Functions to close control gaps, uplift resilience, and maintain auditready evidence.

Job Responsibilities 

  • Lead strategic control testing engagements using analytics to surface gaps and BAU breaks; drive actionable remediation.
  • Build and productionize AI/ML solutions (classification, anomaly detection, risk scoring, entity resolution) and GenAI workflows.
  • Automate endtoend testing and control processes with Alteryx, Power BI/Tableau, and workflow/RPA platforms.
  • Operationalize proactive alerts and dashboards for KRIs and regulatory priorities.
  • Partner with WKO, DDS, Controls, Operations, Technology, and Data teams to define requirements and ensure explainability/auditability.
  • Maintain highquality reporting and executive summaries on trends, systemic issues, and control weaknesses.
  • Stay current on LLMs/GenAI and advanced ML; identify highvalue use cases with appropriate guardrails.
  • Design statistical tests and MLbased monitors for policy, legal, and regulatory compliance aligned to audit and model risk governance.
  • Build pipelines to ingest, profile, cleanse, and join large datasets from systems of record for repeatable analytics.
  • Apply LLMs/GenAI for document parsing, policy mapping, and exception narrative synthesis using RAG and prompt engineering.
  • Translate KYC/CO&D requirements into measurable tests and rules; tune to reduce false positives and document methods for audits.

Required Qualifications, Capabilities, and Skills 

  • Hold a bachelors or advanced degree in a quantitative field (Computer Science, Statistics, Engineering, Data Science).
  • Bring 6+ years in risk management/financial services across compliance, financial crimes, operational risk, audit, or BPM with automation/modeling exposure.
  • Demonstrate proficiency in Python or R for data manipulation, model development, and testing automation at scale.
  • Apply 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.

Preferred Qualifications, Capabilities, and Skills 

  • Leverage handson 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 23 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.
Lead strategic control testing, embedding data, intelligence and automation.