Senior Vice President, Data Management & Quantitative Analysis Manager

BNY; Pune District; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

BNY is seeking a Senior Vice President, AI & Innovation Lead, to guide RCAR AI strategy and drive a portfolio of AI/ML initiatives aimed at risk and compliance outcomes. The role involves leading cross‑functional squads, aligning with enterprise AI frameworks, and embedding responsible AI practices.

You will champion MLOps, model governance, and explainability while partnering with risk leaders to translate domain needs into scalable AI solutions. This role reports to a senior leader within RCAR.

Qualifications

• 8–12 years in data science/AI, with 3–5 years leading AI programs, products, or delivery teams.
• Proven track record delivering production-grade AI/ML solutions with tangible business impact in regulated environments.
• Hands‑on understanding of ML (supervised/unsupervised), NLP/LLMs/GenAI, graph analytics, anomaly detection, time‑series, and decision intelligence.
• Strong command of MLOps (CI/CD for ML, model monitoring, drift management), data engineering, and model evaluation/validation; proficiency with Python and cloud services; containerization and orchestration.
• Practical experience with responsible AI, governance, documentation, validation, and audit readiness; privacy-by-design mindset.
• Experience with prompt engineering and LLM evaluation frameworks; experiment design (A/B testing, hypothesis-driven development).
• Strong stakeholder management and influence skills; translating technical concepts into business outcomes for senior leaders and global teams.
• Candidates should present case studies of prior AI/ML initiatives, governance alignment, and measurable business outcomes.
• Bachelor’s degree required; advanced degree in a relevant discipline preferred.

Responsibilities

• Drive and contribute to the RCAR AI/Innovation strategy and roadmap aligned to enterprise risk priorities and RCAR tenets.
• Develop and manage a balanced portfolio of AI/ML use cases (predictive, prescriptive, GenAI, NLP) targeting material risk and compliance outcomes.
• Drive rigorous prioritization based on business value, risk impact, feasibility, readiness, and time-to-value.
• Partner with ERM R&C AI Strategy & Framework to align with enterprise AI standards, controls, and risk frameworks; collaborate with Risk Engineering and RCAR domain A&R teams to integrate models into production workflows.
• Lead cross-functional squads to design, build, test, and deploy production-grade AI solutions; champion modern engineering practices, data quality, automated testing, and secure-by-design patterns.
• Contribute to and maintain reusable standards and assets (patterns, feature stores, prompt libraries, model cards) to accelerate delivery across RCAR.
• Drive adoption of robust MLOps practices for reliability and scale (versioning, pipelines, monitoring, drift detection, retraining), integrating with enterprise data platforms across cloud/on‑prem.
• Embed responsible AI principles throughout the lifecycle: explainability, fairness, privacy, security, auditability, and human-in-the-loop controls.
• Align with BNY AI governance and model risk management processes; ensure documentation, controls, and approvals are complete prior to production.
• Serve as a trusted partner and advisor to risk and compliance leaders, translating domain needs into AI-driven solutions and measurable outcomes; support change management, training, and adoption with clear communications, playbooks, and enablement across regions and domains.
• Define and track KPIs/OKRs (e.g., cycle time, adoption/utilization, accuracy/precision, false‑positive/negative rates, control efficacy, cost‑to‑serve, latency/throughput, reliability/incident rates, risk decision uplift).
• Conduct post‑implementation reviews; continuously improve with feedback loops across ERM R&C, RCAR horizontal and domain teams, Risk Engineering, and AI Hub.

Skills

AI Strategy

MLOps

Python

Risk & Compliance

LLMs/GenAI

NLP

Data Engineering

Stakeholder Mgmt

Case Studies

Communication

Education

Bachelor's degree

Advanced degree preferred

Tools

Python

Cloud platforms

Docker/Kubernetes

Job description

We are looking to hire a Senior Vice President, AI & Innovation Lead, to join our RCAR (Risk & Compliance Analytics & Reporting) team. This position is based in Pune, with an expectation of 4-5 days per week in the office. The role will report to a senior leader within Risk and Compliance Analytics & Reporting.

In this role, you’ll make an impact in the following ways:

• Drive and contribute to the RCAR AI/Innovation strategy and roadmap aligned to enterprise risk priorities and RCAR tenets.
• Develop and manage a balanced portfolio of AI/ML use cases (predictive, prescriptive, GenAI, NLP) targeted at material risk and compliance outcomes.
• Drive rigorous prioritization based on business value, risk impact, feasibility, readiness, and time‑to‑value.
• Partner closely with ERM R&C AI Strategy & Framework to align with enterprise AI standards, controls, and risk frameworks; collaborate with Risk Engineering and RCAR domain A&R teams to integrate models into production workflows, optimize performance, and ensure resilience and observability.
• Lead and coordinate cross-functional squads to design, build, test, and deploy production-grade AI solutions; champion modern engineering practices, data quality, automated testing, and secure-by-design patterns.
• Contribute to and maintain reusable standards and assets (patterns, feature stores, prompt libraries, model cards) to accelerate delivery across RCAR.
• Drive adoption of robust MLOps practices for reliability and scale (versioning, pipelines, monitoring, drift detection, retraining), integrating with enterprise data platforms across cloud/on-prem.
• Embed responsible AI principles throughout the lifecycle: explainability, fairness, privacy, security, auditability, and human‑in‑the‑loop controls.
• Align with BNY AI governance and model risk management processes; ensure documentation, controls, and approvals are complete prior to production.
• Serve as a trusted partner and advisor to risk and compliance leaders, translating domain needs into AI-driven solutions and measurable outcomes; support change management, training, and adoption with clear communications, playbooks, and enablement across regions and domains.
• Define and track KPIs/OKRs (e.g., cycle time, adoption/utilization, accuracy/precision, false‑positive/negative rates, control efficacy, cost‑to‑serve, latency/throughput, reliability/incident rates, risk decision uplift).
• Conduct post‑implementation reviews; continuously improve with feedback loops across ERM R&C, RCAR horizontal and domain teams, Risk Engineering, and AI Hub.

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

• 8–12 years in data science/AI/advanced analytics, with 3–5 years leading AI programs, products, or delivery teams; prior experience in risk, compliance, audit, or financial services preferred.
• Proven track record delivering production‑grade AI/ML solutions with tangible business impact in regulated environments.
• Hands‑on understanding of ML (supervised/unsupervised), NLP/LLMs/GenAI, graph analytics, anomaly detection, time‑series, and decision intelligence.
• Strong command of MLOps (CI/CD for ML, model monitoring, drift management), data engineering, and model evaluation/validation; proficiency with Python and modern ML stacks; familiarity with cloud services, containerization, and orchestration; working knowledge of enterprise data platforms.
• Practical experience with responsible AI, model governance, documentation, validation, and audit readiness; privacy‑by‑design mindset.
• Experience with prompt engineering and LLM evaluation frameworks (e.g., guardrails, red‑teaming, offline/online evaluation), and with experiment design (A/B testing, hypothesis‑driven development).
• Strong stakeholder management and influence skills, with the ability to translate technical concepts into business outcomes for senior leaders, partners, and global delivery teams.
• Candidates should be prepared to present case studies of prior AI/ML initiatives, governance alignment, and measurable business outcomes.
• Bachelor’s degree required; advanced degree in a relevant discipline preferred.