Applied AI Data Scientist

The Hartford•Hartford, CT

About The Position

Data Scientist - GD08AE We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. The Hartford is expanding its Data Science, AI & Analytics capabilities to deliver next ‑ generation AI, Generative AI, and Agentic AI solutions across underwriting, claims, risk analysis, and enterprise operations. We are seeking an AI Applied Scientist who combines strong scientific depth with hands ‑ on engineering to design, build, evaluate, and operationalize enterprise ‑ grade AI systems. You will collaborate with product owners, SMEs, engineers, and both onshore and offshore DS/ML teams to drive measurable business impact through innovative ML and GenAI solutions.

Requirements

3–5+ years in Data Science, ML Engineering, Applied AI, or GenAI roles.
Strong Python and SQL skills; experience with deep learning and ML libraries.
Hands ‑ on experience building RAG systems, vector search pipelines, and GenAI applications.
Familiarity with modern LLM platforms (Vertex AI, OpenAI, Bedrock, Azure OpenAI).
Experience implementing evaluation methodologies for both ML and GenAI.
Ability to translate ambiguous business problems into well ‑ structured ML/GenAI solutions.
Experience designing multi ‑ step agent workflows and reasoning pipelines.
Strong grounding in statistics, experiment design, and feature engineering.
Ability to present complex concepts clearly to technical and business stakeholders.
Comfortable working with agile teams and collaborating across time zones.
Experience partnering with SMEs to validate and refine AI outputs.
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.

Nice To Haves

Experience with Snowflake or enterprise data platforms.
Insurance domain knowledge (underwriting, claims, or risk analysis).
Experience with multimodal LLMs or enterprise prompt/agent frameworks.

Responsibilities

Build and optimize ML, deep learning, and GenAI models using modern frameworks ( PyTorch , TensorFlow, scikit ‑ learn).
Design and deliver RAG pipelines , hybrid retrieval systems, and vector ‑ based search workflows.
Develop Agentic AI solutions including tool orchestration, reasoning flows, and safe ‑ execution strategies.
Run structured experimentation using evaluation metrics ( BERTScore , BLEURT, semantic similarity, retrieval precision/recall).
Integrate solutions into GCP (Vertex AI, Workbench, Vector Search) and AWS (SageMaker, Bedrock)
Build ingestion, enrichment, and semantic retrieval flows for high ‑ quality knowledge and feature engineering.
Partner with onshore and offshore DS/ML engineers to ensure quality, consistency, and shared technical patterns.