Full -Time Remote:: Senior Data Scientist with Generative AI, Agentic AI & Healthcare Experience

Full-Time Remote:: Senior Data Scientist with Generative AI, Agentic AI & Healthcare Experience

Remote: Dallas, TX

Full Time Remote

JOB SUMMARY

• The Senior Data Scientist, AI executes data science, machine learning, statistical modeling, generative AI, agentic AI, evaluation, and advanced analytics work required to power priority AI use cases and workflow-enabled solutions.
• This role partners closely with AI Product Managers, Data Product Managers, AI Engineers, Software Engineering, Ontology and Knowledge teams, Quality Engineering, Clinical, Operations, Digital, Journey, Privacy, Security, and Governance stakeholders to translate business and clinical questions into rigorous data science solutions.
• The role develops predictive and statistical models, LLM- and agent-based capabilities, analytical frameworks, evaluation approaches, outcome measures, and reusable AI/data science assets that help AI-enabled products improve customer experience, operational efficiency, clinical workflows, personalization, decision support, and enterprise performance.
• This role is expected to combine strong statistical and machine learning skills with hands-on generative AI and agent engineering capabilities, practical product judgment, healthcare data fluency, and the ability to translate ambiguous problems into deployed solutions that work in real operational workflows.
• The Senior Data Scientist, AI reports to the Vice President, Data Science and may operate as either a senior individual contributor or a small-team technical lead, depending on assigned domain and portfolio needs.

ESSENTIAL FUNCTIONS OF THE ROLE

AI Use Case Data Science Delivery

• Translate AI use-case objectives into data science problem statements, modeling approaches, analytical plans, success metrics, and execution roadmaps.
• Develop predictive models, classification models, segmentation approaches, personalization methods, statistical analyses, causal inference approaches, optimization models, simulations, or other traditional data science methods, and combine them with LLM- and agent-based approaches when appropriate to support AI use cases.
• Partner with product, engineering, data, ontology, and clinical/operational stakeholders to ensure data science work is aligned to real workflow needs and measurable outcomes.
• Rapidly prototype, validate, iterate, and productionalize data science, generative AI, and agentic methods in partnership with engineering and platform teams, with a bias toward delivering working capabilities into real user workflows.

Model Development, Validation & Evaluation

• Build, evaluate, tune, and monitor data science and machine learning models using appropriate statistical, ML, and AI methods.
• Define model success criteria, comparison benchmarks, holdout strategies, validation methodologies, error analysis approaches, and ongoing monitoring expectations.
• Develop evaluation approaches for AI-enabled products, including performance measurement, quality thresholds, subgroup analysis, drift detection, fairness considerations, and operational impact tracking.
• Ensure modeling outputs are interpretable, reliable, well-documented, and appropriate for the risk and decision context of the use case.

Generative AI, Agentic AI & Workflow Solution Delivery

• Design, build, evaluate, and deploy LLM-powered applications and agentic solutions, including retrieval-augmented generation (RAG), tool/function calling, structured outputs, reasoning workflows, and multi-step agent patterns.
• Build and orchestrate agents that combine enterprise data, analytical models, APIs, business rules, knowledge/ontology assets, and software tools to perform useful work across analytics, decision support, and operational workflows.
• Select and apply appropriate agent architecture patterns, including single-agent and multi-agent approaches, routing, planning, delegation, memory/context management, human-in-the-loop controls, and deterministic workflow components where reliability requires them.
• Develop and maintain agent tools, prompts/instructions, context and retrieval strategies, orchestration logic, guardrails, and interfaces that enable LLMs and agents to interact safely and reliably with enterprise systems and workflows.
• Create evaluation suites for LLM and agent behavior, including task success, groundedness, factuality, tool-use correctness, workflow completion, latency, cost, safety, robustness, and r…