Senior Manager, Data Science

Xsolis

. Set the vision and multi-year roadmap for data science, ML and agentic AI tied to business goals .

Posted 11/2/2026full-timeRemote • Tennessee • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science and machine learning, with a strong focus on leading teams, developing agentic AI systems, and driving initiatives from concept to production. Proficient in deep learning, GPU training, and MLOps practices, while effectively communicating with diverse stakeholders.

Highest-signal resume keywords

Data Science LeadershipAgentic AI DevelopmentDeep Learning and GPU TrainingMLOps PracticesAWS ML Services

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceMachine LearningDeep LearningGPU TrainingLLM Fine-TuningAgile Project ManagementModel EvaluationAI Product DevelopmentStatistical AnalysisQuantitative Research

Soft Skills

Excellent CommunicationMentoringInfluencing StakeholdersSelf-StarterTeam Leadership

Tools & Technologies

AWS SageMakerAmazon Bedrock AgentCoreDockerTerraformEventBridgeECSCloudWatchCI/CDPEFTLoRA

Industry Keywords

Agentic SystemsAI RoadmapData PrivacyResponsible AIVector Databases

Tech Stack

Tools & technologies

AWSCloudDockerTerraform

About the role

Key responsibilities & impact

Set the vision and multi-year roadmap for data science, ML and agentic AI tied to business goals
Identify high-value AI opportunities and make investment cases to leadership
Track advances in LLMs, agents and fine-tuning and determine adoption timing
Turn loosely defined ideas into scoped bets with goals, milestones and success metrics
Move initiatives from concept to prototype to production and own outcomes end to end
Build, hire and grow a high-performing team of data scientists and ML engineers
Mentor team members through 1:1s, career plans and feedback, and develop future leaders
Establish a culture of ownership, experimentation and high standards
Set standards and review designs for deep learning, GPU and distributed model training
Guide LLM strategy including prompting, RAG and PEFT fine-tuning with LoRA and QLoRA
Ensure rigorous model evaluation and control GPU spending
Shape AWS agentic-system architecture using Amazon Bedrock AgentCore, SageMaker, Lambda, EventBridge, ECS and CloudWatch
Champion MLOps practices including Docker, Terraform, CI/CD and production monitoring
Establish guardrails for agent permissions, data privacy and human review
Partner with product, data engineering and platform leaders on priorities and roadmap
Communicate vision, trade-offs and results to executives and non-technical audiences
Run the team using agile practices including sprint planning, backlog prioritization and retrospectives

Requirements

What you’ll need

8+ years in data science or ML, including 3+ years leading teams of data scientists or ML engineers and experience in building agentic systems
Track record of setting a technical vision and delivering it, ideally taking new AI products or capabilities from idea to production
Strong understanding of practical deployment of agents to production using Agentcore and aligning with the organization’s agentic AI roadmap
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
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, Docker and Terraform
Experience leading agile teams and managing projects across several stakeholder groups
Excellent communication and ability to influence product, engineering and executive stakeholders
Bachelor's degree in computer science, statistics, math or a related field, or equivalent experience
Must successfully pass a background check
Must already be authorized to work in the United States on a full-time, permanent basis without current or future sponsorship
Preferred: experience building a data science or AI function from the ground up, startup or founder experience, presenting strategy to executive leadership, Master's or PhD in a quantitative field, familiarity with RAG, vector databases and agent frameworks, responsible AI practice, and owning cloud and GPU budgets

Benefits

Comp & perks

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