Sr Data Scientist

HCSC•Chicago, IL

21h

About The Position

At HCSC, employees are described as the cornerstone of the business and the foundation of its success. The company offers curated development plans intended to support employee growth and rewarding careers. This position uses advanced mathematical and statistical methods to solve business problems under the direction of more senior staff. The job description outlines data science work that includes collecting, integrating, and analyzing data related to member health status and outcomes, healthcare providers, internal business processes, and other domains. The role performs statistical analysis on experimental or business data to validate and quantify trends or patterns identified by business analysts; uses machine learning and probabilistic modeling to construct predictive algorithms; and verifies model and algorithm effectiveness against real-world results. It also conducts experiments to generate and collect data for business use, and creates visualizations, interactive analytical applications, and interpretive artifacts to support the business application of data science findings. The role aligns with an annual incentive bonus plan, subject to its terms and conditions. Compensation varies with job-related skills, education, knowledge, experience, and location. Flex employees are expected to work in the office at least three days a week; management may modify an employee's 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 an actuarial credential; or a master’s degree and 2 years of relevant work experience; or a Ph.D. in a relevant quantitative field; or 7 years of relevant work experience.
Strong learning and growth mindset.
Customer focus.
Strong interpersonal, verbal, and written communication skills.
Proficiency in at least four of these six areas: data analysis and relational-style query languages; machine learning and/or statistical modeling; data visualization; a high-level programming language; distributed computing; understanding of healthcare.

Nice To Haves

Master’s or Ph.D. in a quantitative field, or a 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, Microsoft Foundry, or comparable platforms, 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 the resulting code or solution.
Hands-on experience designing, building, or evaluating LLM solutions, including prompt engineering, RAG, embeddings, structured outputs, or agentic workflows.
Experience developing agentic AI solutions that use tools, retrieval, and multi-step workflows, with orchestration frameworks such as LangChain, LangGraph, Microsoft Agent Framework, or comparable frameworks.
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 such as hallucination, inaccurate retrieval, and unsuccessful tool use.

Responsibilities

Use advanced mathematical and statistical methods to solve business problems under direction from more senior staff.
Collect, integrate, and analyze data related 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 to support business application of data science findings.