Senior Applied AI & Data Scientist

Job Responsibilities

Below is an opportunity that you might be interested in.

Job Title: Senior Applied AI & Data Scientist

Location: CT, US (Remote)

Type: Long term contract

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 Qualifications

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 Required:

·        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