Senior Vice President, Data Science Manager

Senior Vice President, Data Science Manager

We’re seeking a future team member for the role of Senior Vice President, Data Science Manager to join our Markets team. This role is located in Pune.

In This Role, You’ll Make an Impact in the following ways

This role enables production-grade, governed AI capabilities for Markets by designing, validating, and operating AI agents across their full lifecycle. The individual will own prompt engineering, model selection, RAG/KAG architectures, and ongoing model governance, ensuring AI solutions are explainable, stable, and compliant with DURB and MRMG expectations.

• Excellent knowledge of FX and Fixed Income products pricing, yield curve construction, scenario analysis, sensitivities calculations, PFE, VaR, and CCAR stress scenarios.
• Pricing model development and OPM review for Rates, FX, and Equity models.
• Work with desk strats and quantitative analytics teams to develop, maintain, and support C++/Python analytics libraries used for pricing and risk analytics.
• Define agent logic, behavior, and boundaries for Markets use cases across risk, trading, credit, surveillance, and analytics.
• Design stable, testable, and explainable prompts, with versioning and regression testing.
• Select, justify, and document AI/ML models appropriate to use case criticality and risk tier.
• Design AI architectures, including:
  • Retrieval Augmented Generation (RAG)
  • Knowledge Augmented Generation (KAG)
  • Agent-to-agent communication patterns
  • MCP-style server architectures for tool orchestration and control
• Establish validation and testing strategies so outputs are consistent, trusted, and auditable.
• Ensure AI agents remain assistive, bounded, and compliant over time.
• Produce MRMG / DURB-ready artefacts such as methodology, data lineage, controls, and fallback plans in parallel with development.
• Monitor model and agent behavior in production, including drift, instability, and control breaches.
• Continuously tune prompts, retrieval logic, and models as:
  • New datasets are onboarded
  • Market conditions shift
  • Risk and regulatory expectations evolve
• Own ongoing performance monitoring (OPM) and respond to governance reviews, findings, and control enhancements.
• Hands-on experience supporting DURB and MRMG approvals.
• Ability to translate technical designs into governance-ready documentation.
• Strong understanding of:
  • Model risk management
  • Explainability and auditability
  • MNPI, information barriers, and fiduciary risk
• Comfortable operating under regulatory scrutiny and evolving control frameworks.
• Strong Markets domain awareness across products, workflows, and risk sensitivities.
• Ability to partner with Front Office, Risk, Technology, and Governance stakeholders.
• Capable of balancing delivery speed with control rigor.
• Understands that AI in Markets does not self-govern; prompt quality, model choice, architecture, and validation directly impact market integrity, risk outcomes, and regulatory posture.
• Ensures AI remains trusted, controlled, and scalable as adoption grows.

To be successful in this role, we’re seeking the following:

• Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field, or the equivalent combination of education and experience required; advanced degree preferred.
• Typically 16+ years of relevant experience in data science, analytics, or a related field.
• Proficiency in statistical programming languages such as Python, R, or SQL.
• Strong analytical and problem-solving skills, with the ability to interpret complex datasets and deliver actionable insights.
• Excellent communication skills, with the ability to present data-driven recommendations to both technical and non-technical audiences.
• Strong understanding of model strengths, limitations, and failure modes.
• Model versioning, prompt versioning, and experiment tracking.
• Strong in XGBoost / Gradient Boosting
• Logistic Regression
• Linear Regression
• Time series models
• Clustering (k-means, hierarchical, DBSCAN)
• Dimensionality reduction (PCA, embeddings)
• Transformer-based models
• Large Language Models (LLMs)
• Embedding models for semantic search
• RAG
• KAG
• Semantic search
• Vector databases
• Agent orchestration
• Agent-to-agent communication
• Tool calling and decision gating
• MCP-style server architectures for secure tool access, observability, and control