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. Compensation The wage range for this role takes into account a broad array of factors that are considered in making compensation decisions, including but not limited to: skill sets; experience and training; licensure and certifications; and other business and organizational needs. The range below applies as long as the work is performed in Connecticut; the Company reserves the right to adjust the wage range if the position is performed in another location. At the Company, it is not typical for an individual to be hired at or near the top of the range for their role, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $122,000 - $207,500. Authorization to work in the United States is required. This position is not eligible for visa sponsorship. Physical Demands Must be able to remain in a stationary position for a majority of the workday. Company Cares Our mission is focused on family and faith, and we support our employees in seeking a balanced life. Employee benefits include: Time Away: 13 paid holidays per year in addition to vacation and paid sick leave, and flexible workweek schedules. Professional Development: Certifications, designation, and tuition reimbursement. Retirement Benefits: 401(k) retirement savings plan with matching company contributions, and cash balance retirement plans fully funded by the company. Health and Wellness: Short-term disability and term life insurance fully paid for by the company; Up to 12 weeks of childbirth leave under STD policy. One week of fully paid parental leave for all new parents, including adoptive and foster parents. A variety of health insurance options, including premium-level family coverage and a pre-tax Health Savings Account with employer contributions. The Order's health plans do not cover abortion, sterilization, or contraception, and the Order has helped advocate for other employers who do not want to provide coverage. Long-term disability insurance; Dental insurance; Vision insurance; Health club membership reimbursement; Employee Assistance Program

Requirements

Must-haves

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
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.
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.
Time Away: 13 paid holidays per year in addition to vacation and paid sick leave, and flexible workweek schedules.
Professional Development: Certifications, designation, and tuition reimbursement.
Up to 12 weeks of childbirth leave under STD policy.
Long-term disability insurance;

Nice-to-haves

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).

Application prep

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Keywords to mirror

aimachine learningllmci/cdpythonsqlmldockerapi

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Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Parental Leave

Wellness

Professional Development