Sr Data Scientist

Health Care Service Corporation•Chicago, IL

1d•$92,700 - $167,500

About The Position

The Sr Data Scientist uses advanced mathematical and statistical methods to solve business problems under the direction of more senior staff. The role collects, integrates, and analyzes data related to member health status and outcomes, healthcare provider characteristics, internal business processes, and other domains. It performs statistical analysis on experimental or business data to validate and quantify trends and patterns identified by business analysts; uses machine learning and probabilistic modeling to develop predictive algorithms; and verifies model and algorithm effectiveness using real-world results. The position also conducts experiments to generate and collect data for business use and creates visualizations, interactive analytical applications, and interpretive materials to support business application of data science findings. The role may involve developing and evaluating generative AI and agentic AI solutions, including production deployment in the cloud, LLM application frameworks, retrieval-augmented generation, embeddings, structured outputs, agent workflows, and evaluation methods. It requires explaining technical trade-offs to technical and non-technical stakeholders and validating, testing, and documenting AI-assisted development outputs. HCSC describes its employee environment as purpose-driven and supportive of professional development, with curated development plans. Flex employees are expected to work in the office at least three days per week; management may modify the weekly schedule for exigent circumstances.

Requirements

Bachelor’s degree and 3 years of work experience in a mathematical, statistical, computer science, engineering, physics, economics, or related quantitative field; OR actuarial credential; OR master’s degree and 2 years of relevant quantitative work experience; OR Ph.D. in a relevant quantitative field; OR 7 years of relevant quantitative work experience.
Strong learning and growth mindset.
Customer focus.
Strong interpersonal, verbal, and written communication skills.
Proficiency in at least four of the following six areas: data analysis and relational-style query languages; machine learning and/or statistical modeling; data visualization; a high-level programming language; distributed computing; or understanding of healthcare.

Nice To Haves

Master’s or Ph.D. in a quantitative field, or bachelor’s degree with significant healthcare experience.
Familiarity with GenAI evaluation and LLM observability, such as OpenTelemetry, MLflow Tracing, Arize, or comparable tools.
Experience deploying GenAI or agentic AI to production in the cloud, such as Microsoft Foundry, Databricks Agent Bricks, or comparable platforms.
Understanding of agent architectures, including Model Context Protocol (MCP), agent harnesses, and single- and multi-agent design patterns, with attention to secure tool integration, guardrails, human-in-the-loop controls, and agent tracing.
Experience with enterprise AI platforms such as Databricks or Microsoft Foundry and Python frameworks for LLM applications.
Ability to explain and evaluate trade-offs among accuracy, reliability, latency, cost, security, portability, and implementation complexity for technical and non-technical stakeholders.
Experience using AI-assisted development tools while independently validating, testing, and documenting resulting code or solutions.
Hands-on experience designing, building, or evaluating LLM solutions, including prompt engineering, RAG, embeddings, structured outputs, or agentic workflows.
Experience developing agentic AI solutions using tools, retrieval, and multi-step workflows, with orchestration frameworks such as LangChain, LangGraph, Microsoft Agent Framework, or comparable tools.
Ability to describe specific GenAI or agentic AI solutions developed or contributed to, including the business problem, individual contribution, evaluation approach, and results.
Experience evaluating GenAI outputs using labeled datasets, automated evaluation measures, and human review, including identifying failure modes.

Responsibilities

Collect, integrate, and analyze data relating to member health status and outcomes, healthcare provider characteristics, internal business processes, and other domains.
Perform statistical analysis on experimental or business data to validate and quantify trends or patterns identified by business analysts.
Use machine learning and probabilistic modeling to construct predictive algorithms supporting data analysis or product functions.
Verify model and algorithm effectiveness based on real-world results.
Conduct experiments to generate and collect data for business use.
Create visualizations, interactive analytical applications, and interpretive artifacts that support business application of data science findings.
Evaluate GenAI outputs using labeled datasets, automated measures, and human review, including identifying failure modes such as hallucination, inaccurate retrieval, and unsuccessful tool use.
Explain and evaluate trade-offs among accuracy, reliability, latency, cost, security, portability, and implementation complexity for technical and non-technical stakeholders.
Independently validate, test, and document code or solutions produced with AI-assisted development tools.