Sr AI Data Science Manager

Location:

4910 Tiedeman Road, Brooklyn Ohio

Job Summary 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 meet 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, Lang Chain, OpenAI API) and data manipulation libraries (Pandas, Num Py).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 wledge of financial crime regulatory requirements, technology, and data analysis…