Sr AI Data Science Manager

KeyBank; United States; Hybrid; USD 116,000 - 216,000; Full time

Benefits offered by this job

In-office with flexible options

Incentive compensation eligibility

Job summary

KeyBank is seeking a Senior Manager, Data Science to lead a team of Data Scientists focused on AI-powered solutions across sales enablement, onboarding, servicing, and risk assessment.

You will steer prompt engineering, data curation, model evaluation, and responsible AI practices, balancing hands-on leadership with people management to deliver ethically sound AI solutions aligned with regulatory requirements.

Qualifications

• Master's or Ph.D. in a quantitative discipline with 7+ years in relevant roles, incl. 2+ years of people leadership.
• Proven experience leading data science or AI teams and guiding cross-functional partners.
• Deep expertise in ML/AI modeling, GenAI techniques, and LLM architectures.
• Hands-on Python with ML libraries (Pandas, NumPy) and LLM tooling.
• Knowledge of cloud and distributed computing; familiarity with vector databases and MLOps.

Responsibilities

• Lead and develop a Data Science team, setting goals and delivering multiple AI initiatives.
• Guide generative AI solution strategy, including prompt/context engineering and model evaluation.
• Establish frameworks to evaluate LLM performance and ensure data curation standards.
• Document model configurations and collaborate with risk/compliance teams for safe deployments.
• Promote ethical AI practices and governance while advancing data science strategy.

Skills

Team leadership

Strategic thinking

Python programming

Communication

MLOps awareness

Education

Master's degree in quantitative field

Ph.D. in quantitative discipline

Tools

Hugging Face Transformers

LangChain

OpenAI API

Pandas

NumPy

Vector databases

Job description

ABOUT THE JOB (JOB BRIEF)

Within the Consumer Bank, the Senior Manager, Data Science is responsible for leading and developing a team of Data Scientists focused on identifying, building, and critically evaluating AI-powered solutions across a range of use cases, including sales enablement, marketing content generation, enhanced customer onboarding, intelligent servicing, and novel risk assessment methodologies. Leveraging current and emerging Generative AI technologies, particularly Large Language Models (LLMs), this leader guides the team's work across prompt and context engineering, data curation, model evaluation, responsible AI practices, and evidence-based recommendations that inform solution design. The role balances hands‑on technical leadership with people management, ensuring the team delivers high‑quality, well‑documented, and ethically sound AI solutions that met model risk and regulatory requirements while advancing the bank's broader data science strategy.

Location

4910 Tiedeman Road, Brooklyn Ohio

Job Summary

Within the Consumer Bank, the Senior Manager, Data Science is responsible for leading and developing a team of Data Scientists focused on identifying, building, and critically evaluating AI-powered solutions across a range of use cases, including sales enablement, marketing content generation, enhanced customer onboarding, intelligent servicing, and novel risk assessment methodologies. Leveraging current and emerging Generative AI technologies, particularly Large Language Models (LLMs), this leader guides the team's work across prompt and context engineering, data curation, model evaluation, responsible AI practices, and evidence-based recommendations that inform solution design. The role balances hands‑on technical leadership with people management, ensuring the team delivers high‑quality, well‑documented, and ethically sound AI solutions that met model risk and regulatory requirements while advancing the bank's broader data science strategy.

Essential Job Functions

• Lead, mentor, and develop a team of Data Scientists, setting clear goals, providing technical guidance, and fostering a culture of innovation, accountability, and continuous learning. Manage team workload, prioritization, and delivery across multiple concurrent AI initiatives.
• Support the strategy for developing and implementing generative AI solutions by guiding the team's work in prompt and context engineering, retrieval-augmented generation, fine‑tuning or other model adaptation approaches, experimentation, evaluation methodology, data and ground‑truth requirements, and model behavior analysis, while ensuring adherence to model risk and regulatory requirements.
• Establish and govern internal frameworks for evaluating LLM performance, including metrics for accuracy, fluency, coherence, factual correctness, toxicity, bias, and safety. Ensure consistent standards for data curation, ground truth dataset creation, and adversarial testing across the team.
• Oversee documentation of model configurations, prompt and context strategies, data and ground‑truth assumptions, evaluation methodologies, observed behaviors, limitations, and failure modes. Partner with model validation, compliance, technology, and risk teams to support the safe and ethical deployment of AI applications.
• Champion improvement techniques such as reinforcement learning from human feedback (RLHF), supervised fine‑tuning or other post‑training adaptation methods where appropriate, prompt and context optimization, and bias and safety mitigation to continuously improve output quality, relevance, and trustworthiness across the team's solutions.
• Serve as a key liaison to business, technology, and risk partners, translating complex generative AI concepts, evaluation findings, and potential risks into clear insights for both technical and non‑technical stakeholders. Represent the data science team in cross‑functional planning and executive discussions.
• Build organizational capability by developing team skills and business knowledge, particularly in Generative AI ethics, responsible AI principles, and LLM‑specific risk management, while staying current on emerging technologies and industry best practices.

Required Qualifications

• Master's or Ph.D. in a quantitative discipline and at least 7+ years of relevant experience, including 2+ years of direct people or team leadership.
• Demonstrated experience leading data science or AI teams, with a proven ability to develop talent, manage delivery, set technical standards, and guide cross‑functional partners through complex modeling and evaluation decisions.
• Deep expertise with traditional Machine Learning (ML)/Artificial Intelligence (AI) modeling practices, with demonstrated experience in Generative AI techniques and a strong understanding of LLM architecture, lifecycle methods, evaluation approaches, capabilities, and limitations.
• Hands‑on experience with statistical coding in Python, including LLM libraries and frameworks (e.g., Hugging Face Transformers, LangChain, OpenAI API) and data manipulation libraries (Pandas, NumPy).
• Knowledge of and ability to leverage traditional databases, cloud‑based computing, and distributed computing. Familiarity with vector databases, retrieval systems, knowledge graphs, orchestration patterns, MLOps principles, and monitoring approaches used to support generative AI models at scale.
• Knowledge of financial crime regulatory requirements, technology, and data analysis best practices. Strong understanding of AI ethics principles, responsible AI governance frameworks, and regulatory considerations for AI/LLM deployments.
• Excellent verbal, written, and visual communication skills; ability to translate complex generative AI concepts, evaluation findings, and potential risks to both technical teams and non‑technical executive audiences.

Preferred Qualifications

• Experience in Consumer Banking modeling/analytics, especially in the context of leveraging AI for these domains.
• Experience building and scaling data science teams and establishing LLM evaluation frameworks, including defining and applying relevant metrics.
• Familiarity with prompt injection vulnerabilities and mitigation strategies.

Experience evaluating Retrieval-Augmented Generation (RAG) architectures, knowledge graph integrations, context-engineering patterns, post‑training or model‑adaptation approaches, and LLM evaluation frameworks

Core Competencies

• All KeyBank employees are expected to demonstrate Key’s Values and abide by Key’s Code of Conduct.

Physical Demands

• General Office - Prolonged sitting, ability to communicate face to face in person or on the phone with teammates and clients, frequent use of PC/laptop, occasional lifting/pushing/pulling of backpacks, computer bags up to 10 lbs.

Travel

• Occasional travel to include overnight stay.

Work Location Category

• Hybrid (3+ days)

Compensation And Benefits

This position is eligible to earn a base salary in the range of $116,000.00 - $216,000.00 annually. Placement within the pay range may differ based upon various factors, including but not limited to skills, experience and geographic location. Compensation for this role also includes eligibility for incentive compensation which may include production, commission, and/or discretionary incentives.

Key has implemented an approach to employee workspaces which prioritizes in-office presence, while providing flexible options in circumstances where roles can be performed effectively in a mobile environment.

Job Posting Expiration Date: 10/30/2026 KeyCorp is an Equal Opportunity Employer committed to sustaining an inclusive culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, pregnancy, disability, veteran status or any other characteristic protected by law.

Qualified individuals with disabilities or disabled veterans who are unable or limited in their ability to apply on this site may request reasonable accommodations by emailing [e-mail].