Senior Applied AI & Data Scientist

JOB DESCRIPTION

|

Senior Applied AI & Data Scientist Job Overview Title:

Senior Applied AI & Data Scientist Location:

New Haven, CT Work Mode:

Onsite-C2

H Experience Required:

6+ years

Domain Preference:

Insurance /

Financial Services Key Skills:

Python, SQL, LLM, RAG, Snowflake, ML Deployment Core Responsibilities

• Own end-to-end delivery of AI solutions from problem framing and exploratory analysis through production deployment and measurement.
• Design and deliver LLM-enabled analytics and Deep Research capabilities using RAG over structured and unstructured enterprise data.
• Build agentic workflows and multi-step orchestration (tool use, function calling, retrieval, and guardrails) to automate business processes.
• Develop and deploy advanced statistical and machine learning models supporting insurance, actuarial, claims, risk, and investment decision-making.
• Engineer features and context: build reusable feature pipelines, embeddings, vector search patterns, and semantic/metadata strategies.
• Define success criteria and evaluation plans: offline tests, human-in-the-loop review, and online measurement (A/B or phased rollout).
• Productionize and operate models: partner with engineers to implement CI/CD, monitoring, drift detection, prompt/version management, and incident response runbooks.
• Apply responsible AI practices consistently: bias and fairness assessment, transparency, documentation (model cards), and audit-ready controls.
• Communicate insights and tradeoffs clearly to executives and technical teams (risk, compliance, security) and influence decisions with data.
• Contribute to reusable standards and patterns for MLOps/LLMOps across the enterprise (templates, libraries, and governance checklists).

Skills Required:

• Strong Python and SQL skills; experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch/TensorFlow) and data science best practices.
• Hands-on experience with Snowflake (Snowpark and/or Cortex) and relational databases such as PostgreSQL.
• Experience with vector search/embeddings and knowledge retrieval patterns; familiarity with vector databases and hybrid search.
• Experience partnering with engineering on production services (APIs, batch/stream pipelines), monitoring, and CI/CD.
• Ability to define and run robust evaluation for models/LLMs (quality, safety, performance, cost) and translate into business KPIs.

Preferred:

• Financial services experience (life insurance, annuities, investments preferred) and familiarity with regulated model governance.
• Experience with legacy-to-cloud data modernization (e.g., IBM DB2 extracts) and integration tools (e.g., Talend) where relevant.
• Experience with ML lifecycle tooling (e.

g., MLflow or equivalent), containerization (Docker), and API frameworks (FastAPI/Flask).

Education:

• 6+ years delivering advanced analytics and machine learning solutions, including production deployment.
• 2+ years delivering GenAI/LLM solutions (RAG, agents, evaluation/guardrails) in an enterprise environment.