Senior Applied AI & Data Scientist

Feel Good About Doing Good The Knights of Columbus is a tax-exempt Catholic fraternal benefit society that provides financial security to members and their families through our life insurance, long-term care insurance, disability income insurance, investment and annuity products. Charity is at the core of our missions: our profits are donated to help those in need and to support our faith - $1.73B over the past ten years.

While we have many employees who are not Catholic, we follow the Church’s teachings in our investment strategies and our employee benefits. As part of our religious mission, we support the pro-life cause by contributing to the March for Life and pregnancy resource centers, we oppose assisted suicide and euthanasia, we are evangelists for the Catholic faith, and we help Christians who are facing religious persecution in the Middle East.

We all work together to support our two million members as they volunteer to help others in their parishes and communities around the world.

Share Your Talent. Live Your Purpose. We are a growing and purpose-driven community of professionals. Join us to discover how you can meet your goals and ours!

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, Postgre

SQL, and approved AI platforms including Retrieval Augmented Generation (RAG), agentic workflows, and decision support analytics with clear responsible AI controls suitable for regulated environments.

• 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).

• Strong Python and SQL skills; experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch/Tensor Flow) and data science best practices.
• Hands-on experience with Snowflake (Snowpark and/or Cortex) and relational databases such as Postgre
  
  SQL.
• 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…