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Senior Vice President, Data Management & Quantitative Analysis Manager
senior · Risk & Compliance
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.