Senior Applied AI & Data Scientist

• The Senior Applied AI & Data Scientist is a senior-level individual contributor who designs, builds, evaluates, and deploys AI-powered solutions that deliver measurable business outcomes across Life Insurance, Investments, Membership, and Charities.
• This role blends advanced analytics, machine learning, and generative AI (LLMs) with strong product and engineering execution operating within a governed, enterprise-scale data and AI ecosystem.
• Reporting to the Director of Applied AI & Data Science, this role partners closely with business stakeholders, data analysts, engineers, and Data & AI architects to translate complex questions into explainable and production-ready capabilities.
• You will build solutions using Snowflake, PostgreSQL, and approved AI platforms including Retrieval Augmented Generation (RAG), agentic workflows, and decision support analytics with clear responsible AI controls suitable for regulated environments.

Job Description

• The Senior Applied AI & Data Scientist is a senior-level individual contributor who designs, builds, evaluates, and deploys AI-powered solutions that deliver measurable business outcomes across Life Insurance, Investments, Membership, and Charities.
• This role blends advanced analytics, machine learning, and generative AI (LLMs) with strong product and engineering execution operating within a governed, enterprise-scale data and AI ecosystem.
• Reporting to the Director of Applied AI & Data Science, this role partners closely with business stakeholders, data analysts, engineers, and Data & AI architects to translate complex questions into explainable and production-ready capabilities.
• You will build solutions using Snowflake, PostgreSQL, and approved AI platforms including Retrieval Augmented Generation (RAG), agentic workflows, and decision support analytics with clear responsible AI controls suitable for regulated environments.

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.

Physical Demands

Must be able to remain in a stationary position for a majority of the workday.