Sr AI Data Science Manager
About The Position
Within the Consumer Bank, the Senior Manager, Data Science leads and develops a team of Data Scientists identifying, building, and critically evaluating AI-powered solutions for use cases such as sales enablement, marketing content generation, customer onboarding, intelligent servicing, and risk assessment. The role guides work involving Generative AI and Large Language Models (LLMs), including prompt and context engineering, data curation, model evaluation, responsible AI practices, and evidence-based recommendations for solution design. This position combines hands-on technical leadership with people management. The manager is accountable for delivering high-quality, well-documented, ethically sound AI solutions that meet model risk and regulatory requirements while advancing the bank’s data science strategy. The role partners with business, technology, validation, compliance, and risk teams and translates technical concepts, evaluation findings, and risks for technical and non-technical stakeholders. The position is based at 4910 Tiedeman Road, Brooklyn, Ohio, and is categorized as hybrid, with three or more days in the office. Occasional travel, including overnight stays, is expected. The listed annual base salary range is $116,000 to $216,000, with eligibility for incentive compensation. The job posting expires October 30, 2026.
Requirements
- Master’s or Ph.D. in a quantitative discipline.
- At least 7 years of relevant experience, including at least 2 years of direct people or team leadership.
- Demonstrated experience leading data science or AI teams, developing talent, managing delivery, setting technical standards, and guiding cross-functional partners through complex modeling and evaluation decisions.
- Deep expertise in traditional ML/AI modeling practices, demonstrated experience with Generative AI techniques, and strong understanding of LLM architecture, lifecycle methods, evaluation approaches, capabilities, and limitations.
- Hands-on statistical coding experience in Python, including LLM libraries and frameworks such as Hugging Face Transformers, LangChain, or OpenAI API, and data manipulation libraries such as Pandas and NumPy.
- Knowledge of and ability to use traditional databases, cloud-based computing, and distributed computing.
- Familiarity with vector databases, retrieval systems, knowledge graphs, orchestration patterns, MLOps principles, and monitoring approaches for 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, including the ability to explain generative AI concepts, evaluation findings, and risks to technical teams and non-technical executives.
Nice To Haves
- Experience in Consumer Banking modeling or analytics, particularly applying AI in 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 RAG architectures, knowledge graph integrations, context-engineering patterns, post-training or model-adaptation approaches, and LLM evaluation frameworks.
Responsibilities
- Lead, mentor, and develop a team of Data Scientists by setting goals, providing technical guidance, and fostering innovation, accountability, and continuous learning.
- Manage team workload, prioritization, and delivery across concurrent AI initiatives.
- Guide development and implementation of generative AI solutions, including prompt and context engineering, retrieval-augmented generation, fine-tuning or other model adaptation, experimentation, evaluation methodology, data and ground-truth requirements, and model behavior analysis.
- Ensure the team’s work adheres to model risk and regulatory requirements.
- Establish and govern internal frameworks for evaluating LLM performance, including accuracy, fluency, coherence, factual correctness, toxicity, bias, and safety metrics.
- Ensure consistent team standards for data curation, ground-truth dataset creation, and adversarial testing.
- Oversee documentation of model configurations, prompt and context strategies, data and ground-truth assumptions, evaluation methods, observed behaviors, limitations, and failure modes.
- Partner with model validation, compliance, technology, and risk teams to support safe and ethical AI deployment.
- Champion improvement methods such as RLHF, supervised fine-tuning and other post-training adaptation, prompt and context optimization, and bias and safety mitigation.
- Translate generative AI concepts, evaluation findings, and potential risks for technical and non-technical stakeholders.
- Represent the data science team in cross-functional planning and executive discussions.
- Develop team skills and business knowledge, particularly in Generative AI ethics, responsible AI principles, and LLM-specific risk management, and stay current on emerging technologies and industry practices.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
