Senior Manager, Data Science

Senior Manager, Data Science

Xsolis is an AI-driven technology company reducing administrative waste in healthcare by enabling smarter collaboration between providers and payers. Since 2013, our vision has been to create a frictionless healthcare system.

We are proudly headquartered in Franklin, TN - one of the nation's fastest-growing hubs for healthcare innovation - we are at the heart of a thriving ecosystem of health systems, payers, and technology leaders.

Our flagship product, Dragonfly®, is the first and only AI-driven platform to continuously assign real-time predictive scores for medical necessity. Dragonfly has delivered proven results across 600+ hospital, health system, and health plan clients and earned repeated industry recognition. With recognition on the Inc. 5000 and Deloitte Technology Fast 500 lists, Xsolis is also one of the fastest-growing privately-held companies in the country and across North America, and we're looking for talented people to grow with us.

Position Summary Details Xsolis is hiring a Senior Manager, Data Science to set the vision for how we use predictive, generative and agentic AI, and to build and lead the team that delivers it. You will spot the opportunities worth pursuing, shape them into a strategy, and take them from idea to production.

This is a leadership role with expectation on deep hands-on coding knowledge. We want a self-starter who doesn't wait for a roadmap: someone who can take a rough idea, rally a team around it and run with it. You will have the technical depth to guide decisions on model training, LLM fine-tuning and agent design, and you will spend your time leading people and driving outcomes in partnership with product and engineering teams

Essential Duties & Responsibilities The essential functions include, but are not limited to the following:

Vision and strategy

• Define a multi-year vision and roadmap for data science, ML and agentic AI, tied to business goals.

• Identify where AI creates the most value and make the case to leadership for where to invest.

• Track where the field is heading (LLMs, agents, fine-tuning) and decide what the team adopts, and when.

Taking ideas from zero to one

• Take loosely defined ideas and turn them into scoped bets with clear goals, milestones and success metrics.

• Move quickly from concept to prototype to production without waiting for a fully formed plan.

• Own outcomes end to end, and know when to push forward, pivot or stop.

Team leadership

• Build, hire and grow a high-performing team of data scientists and ML engineers.

• Mentor through regular 1:1s, career plans and honest feedback, and develop future leaders on the team.

• Create a culture of ownership, experimentation and high standards.

• Delegate the technical work and empower the team to make decisions.

Technical direction: models and LLMs

• Set standards and review designs for training deep learning models, including GPU and distributed training (data, model and pipeline parallelism).

• Guide LLM strategy: when to prompt, use RAG, or fine-tune with PEFT methods such as LoRA and QLoRA.

• Make sure models are evaluated rigorously before and after they ship, and that GPU spend stays under control.

Technical direction: agentic AI and infrastructure

• Shape the architecture for agentic systems on AWS: Amazon Bedrock AgentCore, SageMaker, Lambda, EventBridge and ECS, with CloudWatch for observability.

• Champion MLOps practices: Docker, infrastructure as code with Terraform, CI/CD for models and production monitoring.

• Set guardrails for safe, responsible AI: agent permissions, data privacy and human review.

Stakeholders and delivery

• Partner regularly with product, data engineering and platform leaders to align on priorities and roadmap.

• Communicate vision, trade-offs and results clearly to executives and non-technical audiences.

• Run the team in an agile cadence (sprint planning, backlog prioritization, retros) with predictable delivery.

Minimum Qualifications (Knowledge, Skills, and Abilities)

• 8+ years in data science or ML, including 3+ years leading teams of data scientists or ML engineers and experience in building agentic systems

• A track record of setting a technical vision and delivering it, ideally taking new AI products or capabilities from idea to production.

• Strong understanding on practical deployment of agents to production using Agentcore and aligning with the organization's agentic AI roadmap.

• A self-starter who thrives in ambiguity and drives initiatives without needing detailed direction.

• Deep understanding of deep learning and GPU training at scale, including distributed training, enough to guide the team and challenge its decisions.

• Strong grasp of LLM fine-tuning (PEFT, LoRA, QLoRA) and agentic AI architecture.

• Familiarity with AWS ML and serverless services (SageMaker, Bedrock AgentCore, Lambda, EventBridge, ECS), Docker and Terraform.

• Experience leading agile teams and managing projects across several stakeholder groups.

• An excellent communicator who can influence product, engineering and executive stakeholders.

• Bachelor's degree in computer science, statistics, math or a related field, or equivalent experience.

Preferred qualifications

• Experience building a data science or AI function from the ground up, or startup or founder experience.

• Experience presenting strategy to executive leadership

• Master's or PhD in a quantitative field.

• Familiarity with RAG, vector databases and agent frameworks such as LangGraph, Strands Agents or MCP.

• Responsible AI practice: bias testing, safety evaluations and data governance.

• Experience owning cloud and GPU budgets.

Working Environment and Travel Requirements

• Work is typically in a normal office administrative environment involving minimal exposure to physical risks.
• Position requires little to moderate physical activity. Mostly sedentary work exerting up to 10 pounds of force occasionally or a negligible amount of force to lift, carry, push, pull, or otherwise move objects. Work involves sitting most of the time, but may involve walking or standing for brief periods of time. No significant stooping is usually required.
• This role requires travel to our home offices in TN for training.

Physical Demands & Work Environment The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the functions.

In any role at Xsolis, it's a chance to make a complex healthcare system a little more efficient alongside a team that's passionate, fast-moving, and genuinely invested in your success. Your contributions will shape both our product and our culture.

Full-time Employees' Total Rewards package include:

• Medical/Dental/Vision insurance
• 401K company match
• Flexible Paid Time Off
• Paid parental leave
• HSA/FSA options
• Educational reimbursement program
• Employer-paid EAP & Mental Health services

Xsolis is proud to be an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all. We encourage candidates of every background to apply.