Among computer and information systems managers, AI is already used for some of the tasks. Measured exposure for computer and information systems managers: 16% (some).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

Senior Vice President - Data Science and Artificial Intelligence Group Manager - Hybrid Apply (opens in new window)

Job Req Id:

26990936

Location(s):

New York, New York, United States, Wilmington, Delaware, United States

Job Type:

Hybrid

Posted:

Oct. 19, 2026

Discover your future at Citi

Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.

Job Overview

About the Role

We are looking for a technically deep, strategically minded leader to own Partner AI Development & Delivery and GenAI Model Governance . This role sits at the intersection of Generative AI innovation, enterprise model governance, and regulatory compliance — with end-to-end accountability across high-visibility programs that shape business outcomes at scale.

You will lead a multi-geography team, engage MD-level stakeholders across Operations, Marketing, Products, Technology, Model Risk, Fair Lending, and Legal, and deliver across expense reduction, revenue growth, customer experience improvement, compliance transformation, and enterprise GenAI capability buildout.

What You'll Deliver

1. Banking Operations AI Automation Partner with business units and Technology to deliver GenAI-powered automation across front, mid, and back office banking operations — including call center IVR, Agent Assist, Collections, and other operational functions. Combine technical depth with deep banking domain knowledge to address complex, cross-functional challenges at scale.

2. Foundation GenAI Capabilities Support enterprise-wide foundational GenAI capabilities including LLM fine-tuning and semantic layer development — enabling scalable, data-driven decision-making across business functions.

3. GenAI Solution Development Own the full AI solution lifecycle — concept to production — for high-priority business problems including Personalized Next Best Action decisioning, Campaign Analytics, and Conversational & Contextual Business Insights.

4. Analytics Workflow Transformation Conceptualize AI-powered tools that accelerate analytics workflows and drive productivity. Lead the evolution of AI-enhanced modeling techniques to improve development speed and precision.

5. GenAI Model Portfolio Governance Manage a large GenAI model portfolio across its full lifecycle — tuning, upgrades, and maintenance. Serve as Model Sponsor Delegate , with formal accountability for model ownership, sign-off, and governance under Model Risk Policy.

6. Enterprise GenAI Capabilities Build firm-wide GenAI infrastructure: adversarial testing, regulatory testing, guardrails, near real-time monitoring, and AI governance policy co-development with the second line of defense.

7. Measurable Productivity & Delivery Establish frameworks that track outcomes across business deliverables and team performance — aligned with senior leadership priorities.

How You'll Deliver

Build a High-Performing AI Team Recruit top talent and lead a specialized team delivering enterprise-grade AI. Foster a culture of innovation focused on non-linear outcomes.

Partner with Central AI and Business Teams Execute strategically with the firm's Central AI organization and business units to redesign processes and deliver measurable change.

Align on Priorities Work closely with business stakeholders and analytics leadership to identify, prioritize, and resource high-impact AI solutions — enabling rapid alignment and efficient delivery.

Maintain a Robust Controls Mindset Ensure teams follow control processes across Information Security, Platform Security, and Data Security while executing on next-generation AI re-engineering of business and data processes.

Qualifications

Experience

10+ years in AI/ML, data science, or quantitative analytics within financial services

Proven expertise in Generative AI, LLMs, and applied GenAI product development in production environments

Track record in model risk management, regulatory remediation, and formal model governance

Experience managing large-scale AI/ML model portfolios including lifecycle, tuning, and upgrades

Deep data domain knowledge across core banking functions — including marketing, customer management, call center operations, collections, complaints, and financial controls — with the ability to connect data signals to business outcomes

Strong understanding of credit card economics, customer behavior, and product strategy at the intersection of data and finance

Experience with Model Risk Management, Fair Lending, and Compliance in a regulated financial institution

Technical Skills

GenAI & ML: Generative AI, LLMs, RAG, Agentic AI, prompt engineering, LLM fine-tuning, NLP, supervised/unsupervised learning, Mathematical Optimization & Decision Science

Infrastructure & Ops: MLOps/LLMOps, cloud-native AI platforms, big data, distributed computing, Spark, Python, SQL

Governance: Adversarial testing, guardrails, regulatory testing frameworks, near real-time model monitoring

Tooling: SDLC, version control (Bitbucket/GitHub), JIRA, DevOps

Regulatory: SR 11-7, SR 26-2, OCC AI guidance, CFPB, Fair Lending, ECOA — in the context of AI/ML deployment within a regulated institution

Leadership & Communication

Proven ability to lead distributed, multi-geography teams in a global, matrixed organization

Exceptional stakeholder management — engage, influence, and negotiate at the MD level

Strong program management across complex, multi-workstream initiatives with regulatory and executive timelines

Ability to translate technical complexity into clear narratives for senior audiences

Board-ready executive communication and presentation skills

Education

Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, or Economics) required

Master's degree or Ph.D. strongly preferred

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Job Family Group: Decision Management ------------------------------------------------------

Job Family: Specialized Analytics (Data Science/Computational Statistics) ------------------------------------------------------

Time Type: Full time ------------------------------------------------------ </s