Sr Data Scientist- Generative AI

Citizens•Columbus, OH

14h•Hybrid

About The Position

Join a team where innovation meets impact. As a Senior Data Scientist, Generative AI & Agentic Systems, you will help drive the bank's AI transformation by designing, developing, and deploying Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) systems, AI agents, and intelligent automation capabilities. You will work across business, technology, risk, and compliance teams to deliver responsible, scalable, and production-ready GenAI solutions that improve customer experiences, enhance operational efficiency, and create measurable business value. This role is ideal for an experienced data scientist with strong software engineering and machine learning skills, deep expertise in NLP and Generative AI, and experience developing AI solutions within highly regulated environments.

Requirements

Ph.D. or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or a related quantitative field.
7+ years of experience in data science, machine learning, predictive analytics, or artificial intelligence.
4+ years of hands-on experience developing NLP and Generative AI solutions.
Strong proficiency in Python and modern software development practices.
Experience developing and deploying LLM-based applications using commercial or open-source models.
Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search.
Experience with prompt engineering, prompt evaluation, and LLM performance optimization.
Strong understanding of machine learning algorithms, deep learning, statistical modeling, and model explainability techniques.
Experience working with structured and unstructured data at enterprise scale.
Experience collaborating with cross-functional stakeholders and communicating technical concepts to non-technical audiences.
Strong knowledge of model governance, validation processes, and documentation standards.

Nice To Haves

Experience designing and deploying AI agents and multi-agent systems.
Experience with agent orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, Autogen, or similar technologies.
Experience serving open-source LLMs using vLLM, Hugging Face, or equivalent inference frameworks.
Experience with RAG evaluation frameworks such as RAGAS or other LLM evaluation methodologies.
Experience with model monitoring, MLOps, and production AI deployment.
Experience with cloud AI platforms such as AWS Bedrock, Azure AI, Databricks, Snowflake Cortex.
Experience building document intelligence solutions involving PDFs, OCR, document extraction, knowledge extraction from images, and workflow automation.
Experience within banking, financial services, fintech, insurance, or other regulated industries.
Experience supporting Model Risk Management (MRM), model validation, audit reviews, or regulatory examinations.
Familiarity with MCP (Model Context Protocol), tool calling frameworks, and AI workflow automation platforms.

Responsibilities

Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms.
Build intelligent document processing capabilities for information extraction, summarization, classification, question answering, and conversational AI applications.
Develop agentic workflows capable of autonomous reasoning, task execution, tool utilization, and multi-step decision support.
Design and implement retrieval pipelines, vector search architectures, embedding strategies, and knowledge-grounded AI systems.
Evaluate and improve LLM performance through prompt engineering, model benchmarking, hallucination reduction, and faithfulness testing.
Build scalable AI solutions using modern frameworks and infrastructure including vLLM, LangChain, LangGraph, MLflow, Databricks, Snowflake, and cloud-native platforms.
Perform exploratory data analysis, feature engineering, and statistical analysis to support machine learning and GenAI model development.
Develop model monitoring, evaluation, and observability frameworks to measure quality, reliability, fairness, and operational performance.
Collaborate closely with Model Risk Management (MRM), Compliance, Audit, Legal, and Information Security teams to ensure responsible AI deployment.
Create technical documentation, model development artifacts, validation packages, and executive-level presentations.
Partner with product managers, engineers, data architects, and business stakeholders to identify and prioritize GenAI opportunities.
Stay current with advances in Generative AI, agentic systems, multimodal AI, foundation models, and emerging industry best practices.