Senior Data Scientist - Growth AI

Humana

1d • Remote

About The Position

The Senior Data Scientist drives impactful AI and analytics projects that support business growth and operational excellence. You will develop both ad-hoc and production-ready AI applications, working closely with business stakeholders to provide applicable solutions. While you will collaborate with infrastructure and platform teams and operate within established cloud and container standards, you will focus on data science, software development, and application delivery. We are looking for an experienced Senior Data Scientist to support AI and advanced analytics projects that allow Medicare growth and bid strategy. The Senior Data Scientist will work closely with business stakeholders, engineers, and subject matter experts to turn ambiguous problems into practical AI solutions. In addition to strong data science and AI capabilities, we are looking for a practical software engineering mindset. The ideal candidate will be comfortable developing modular, maintainable code. You will also use version control and collaborative development workflows, support CI/CD processes, and work with containerized applications. Furthermore, they will contribute to reliable deployment and monitoring practices for data and AI solutions in production environments.

Requirements

• Bachelor's degree and 5+ years (or Master's and 3+ years) of experience in data science, computer science, engineering, or a related field.
• 2+ years of hands-on experience building LLM-based applications for enterprise use cases.
• Advanced proficiency in Python, with hands-on experience developing APIs (preferably with FastAPI).
• Experience containerizing applications with Docker.
• Familiarity with deployment pipelines (e.g., Azure Pipelines) and working within enterprise cloud environments collaborating with infrastructure/platform teams.
• Experience building agentic systems using tool-calling, routing, planning/execution patterns, context management, and structured decision flows to solve multi-step enterprise problems reliably and at production quality.
• Demonstrated ability to deliver production-ready data science solutions with minimal oversight.
• Excellent collaboration and communication skills for both technical and business audiences.
• Understanding of data governance, security, and compliance considerations when handling sensitive data and deploying solutions into production environments.

Nice To Haves

• Experience in healthcare or regulated industries.
• Familiarity with version control and collaborative development tools (e.g., Git).
• Experience integrating models into operational workflows and APIs.
• Familiar with retrieval-grounded LLM systems, including retrieval-augmented generation (RAG) approaches that produce grounded, verifiable outputs.
• Familiar with AI applications that integrate LLMs with web search and grounding tools, including retrieval strategy, citation fidelity, guardrails, observability, and evaluation.
• Experience using Databricks and Spark to support data processing and pipeline development, including common data lake patterns.
• Experience supporting evaluation and monitoring practices for retrieval and extraction quality, including golden datasets, regression testing, groundedness checks, and production observability.
• Experience integrating LLM-based agents with enterprise tools.

Responsibilities

• Independently design, build, and deploy AI and machine learning models for business-critical and ad-hoc applications.
• Design and implement agentic AI workflows that decompose complex business tasks into multi-step actions involving retrieval, reasoning, tool use, and rule-based decisioning.
• Implement monitoring and evaluation practices for LLM applications, including output quality, groundedness, latency, drift, and user feedback loops.
• Develop well-structured, maintainable, and testable code (e.g., using Python and FastAPI) for robust AI solutions.
• Package and prepare applications for deployment using Docker, following internal standards for containerization.
• Participate in deployment and release workflows (e.g., Azure Pipelines) in partnership with platform teams, ensuring compliance with cloud and security standards.
• Collaborate directly with business stakeholders to translate requirements into technical solutions.
• Work with infrastructure and DevOps teams to ensure that AI applications are deployed and operated reliably in AKS, GKE, or other approved environments.
• Participate in code reviews, contribute to shared codebases, and follow best practices in version control and documentation.
• Monitor, evaluate, and refine deployed models and applications as needed.
• Use your skills to make an impact

Benefits

• medical, dental and vision benefits
• 401(k) retirement savings plan
• time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave)
• short-term and long-term disability
• life insurance

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees