Senior Manager, Data Science

Xsolis, Inc

About The Position

Xsolis is hiring a Senior Manager, Data Science to set the vision for the company’s use of predictive, generative, and agentic AI and build and lead the team that delivers it. The role identifies valuable AI opportunities, turns them into strategy, and takes initiatives from idea through production. It combines people leadership and outcome ownership with deep technical knowledge of model training, LLM fine-tuning, and agent design, in partnership with product and engineering teams. The Senior Manager will establish a multi-year data science, machine learning, and agentic AI roadmap tied to business goals; build and develop a high-performing data science and ML engineering team; and guide technical decisions involving deep learning, distributed GPU training, LLMs, and agentic systems. The role also promotes MLOps, responsible AI safeguards, and predictable agile delivery, while communicating strategy and results to executives and cross-functional stakeholders. Work is typically in a normal office administrative environment with minimal exposure to physical risks. The job involves mostly sedentary work and little to moderate physical activity. Travel to Xsolis’s home offices in Tennessee is required for training.

Requirements

At least 8 years of experience in data science or machine learning.
At least 3 years of experience leading teams of data scientists or ML engineers.
Experience building agentic systems.
A track record of setting technical vision and delivering it, ideally taking new AI products or capabilities from idea to production.
Strong understanding of practical production deployment of agents using AgentCore and alignment with an organization’s agentic AI roadmap.
Self-starter who thrives in ambiguity and drives initiatives without detailed direction.
Deep understanding of deep learning and GPU training at scale, including distributed training, sufficient to guide the team and challenge its decisions.
Strong grasp of LLM fine-tuning, including PEFT, LoRA, and QLoRA, and agentic AI architecture.
Familiarity with AWS ML and serverless services, including SageMaker, Bedrock AgentCore, Lambda, EventBridge, and ECS, as well as Docker and Terraform.
Experience leading agile teams and managing projects across multiple stakeholder groups.
Excellent communication skills and ability to influence product, engineering, and executive stakeholders.
Bachelor’s degree in computer science, statistics, mathematics, or a related field, or equivalent experience.
Must already be authorized to work in the United States on a full-time, permanent basis without current or future visa sponsorship or immigration support.
Must successfully pass a background check; the role includes access to sensitive information, including proprietary data and PHI.

Nice To Haves

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 experience, including bias testing, safety evaluations, and data governance.
Experience owning cloud and GPU budgets.

Responsibilities

Define a multi-year vision and roadmap for data science, machine learning, and agentic AI aligned with business goals.
Identify high-value AI opportunities and make investment cases to leadership.
Monitor developments in LLMs, agents, and fine-tuning and determine which technologies the team should adopt and when.
Turn loosely defined ideas into scoped initiatives with goals, milestones, and success metrics.
Move initiatives from concept through prototype to production, owning outcomes and deciding when to proceed, pivot, or stop.
Build, hire, and develop a high-performing team of data scientists and ML engineers.
Mentor team members through regular one-to-ones, career plans, and feedback, and develop future leaders.
Create a culture of ownership, experimentation, and high standards; delegate technical work and empower team decisions.
Set standards and review designs for deep learning model training, including GPU and distributed training approaches.
Guide decisions on whether to use prompting, retrieval-augmented generation (RAG), or PEFT fine-tuning methods such as LoRA and QLoRA.
Ensure rigorous model evaluation before and after deployment and control GPU spending.
Shape AWS agentic-system architecture using services including Amazon Bedrock AgentCore, SageMaker, Lambda, EventBridge, ECS, and CloudWatch.
Champion MLOps practices, including Docker, Terraform-based infrastructure as code, model CI/CD, and production monitoring.
Set responsible AI safeguards for agent permissions, data privacy, and human review.
Partner with product, data engineering, and platform leaders to align priorities and roadmaps.
Communicate vision, trade-offs, and results to executive and non-technical audiences.
Run the team using an agile cadence, including sprint planning, backlog prioritization, and retrospectives, to support predictable delivery.