GEN AI Sr Data Scientist/Data Scientist

Company	Hartford Financial Services
Location	Chicago, IL, USA, Charlotte, NC, USA, Columbus, OH, USA, Hartford, CT, USA
Salary	$90160 – $166080
Type	Full-Time
Degrees	Master’s, PhD
Experience Level	Mid Level, Senior

Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Engineering or a related field.
3+ years of industry experience in machine learning or data science and with 1+ years focused on GenAI.
Proven experience in GenAI tools such as VertexAI/Google agent development kit, LangChain/LangGraph, RAG frameworks, HuggingFace, OpenAI APIs, etc.
Solid understanding of prompt engineering, retrieval-augmented generation (RAG), agent workflow, and LLM evaluation.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, etc.
Experience with vector databases such as Chroma, Pinecone, etc. and cloud platforms such as GCP, AWS, Azure, etc.
Strong analytical, problem solving and debugging skills.
Excellent communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders across the enterprise.
Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Support The Hartford’s AI innovation strategy to identify and implement high-end use cases, ensuring alignment with business objectives and emerging industry trends.
Support cutting-edge AI vision and roadmap for the enterprise to make The Hartford a leader in using AI for the insurance industry.
Partner with stakeholders internal to the Data Science organization, Technology organization and business organization, aligning with business objectives and driving innovation to improve productivity and efficiency of underwriting and sales efforts.
Design and build GenAI solutions for tasks such as summarization, question answering, search, data synthesis, etc.
Rapidly prototype with cutting-edge tools, such as VertexAI/Google agent development kit, LangChain/LangGraph, RAG frameworks, HuggingFace, OpenAI APIs, etc.
Research and recommend novel foundation model options, tools, and libraries to improve solutions in performance and reliability.
Develop evaluation pipelines for generative outputs using a mix of automatic metrics, human feedback, and llm-as-a-judge methods.
Collaborate with ML Ops to ensure best practice is followed for scalable training and deployment.

No preferred qualifications provided.