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Vp Data Science Jobs in Tennessee (NOW HIRING)

NxT Level

Vice President of AI

Nashville, TN · On-site

Vice President of AI Strategy & Transformation Location: Flexible (Hybrid/Remote Options Available ... Develop intelligent assistants, workflow automations, and internal data agents to streamline ...

NxT Level

Vice President of AI

Nashville, TN · On-site +1

Vice President of AI Strategy & Transformation Location: Flexible (Hybrid/Remote Options Available ... Develop intelligent assistants, workflow automations, and internal data agents to streamline ...

Honest Medical Group LLC

VP, Finance

Nashville, TN · On-site

$237.40 - $356.20/hr

Your Role The Vice President, Finance is a key senior leader and strategic partner to the Chief ... Translate complex financial data into clear insights for internal and external stakeholders.

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All JobsVp Data Science JobsVp Data Science Jobs in Tennessee

Vp Data Science information

See Tennessee salary details

$37.7K

$129.3K

$182.4K

How much do vp data science jobs pay per year?

As of Aug 8, 2026, the average yearly pay for vp data science in Tennessee is $129,299.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,600.00 and $151,100.00 per year, depending on experience, location, and employer.

What does a VP of Data Science do?

A VP of Data Science leads and manages the data science team within an organization, setting the strategic vision for how data is used to drive business decisions. They oversee the development and implementation of data-driven solutions, ensure data quality and integrity, and collaborate with other executives to align data initiatives with company goals. Additionally, they mentor data scientists, manage budgets, and stay updated on the latest trends and tools in data science to keep their teams competitive.

What are the key skills and qualifications needed to thrive as a VP of Data Science?

To thrive as a VP of Data Science, you need advanced expertise in statistical analysis, machine learning, and big data, usually supported by a graduate degree in a quantitative field and extensive industry experience. Familiarity with tools like Python, R, SQL, cloud platforms (e.g., AWS, Azure), and data visualization systems, as well as experience managing enterprise data architectures, is crucial. Exceptional leadership, strategic thinking, and communication skills set top candidates apart in this role. These abilities are essential for guiding teams, influencing business decisions, and driving impactful data-driven strategies across the organization.

How does a VP of Data Science typically collaborate with cross-functional teams to drive business outcomes?

A VP of Data Science frequently works with product managers, engineering teams, and business stakeholders to align data initiatives with organizational goals. They play a strategic role in translating business challenges into data-driven solutions, ensuring that data science projects support decision-making and growth. Effective collaboration involves regular meetings, clear communication of technical concepts to non-technical audiences, and fostering a culture of data literacy across the organization. By bridging technical expertise and business acumen, the VP helps maximize the impact of data science initiatives.

What is the difference between Vp Data Science vs Data Science Manager?

AspectVp Data ScienceData Science Manager
ResponsibilitiesStrategic leadership, setting data science vision, overseeing multiple teamsManaging data science projects, team supervision, project delivery
Required CredentialsAdvanced degree (Master's/PhD), extensive experience, leadership skillsDegree in related field, experience in managing data projects
Work EnvironmentExecutive-level, cross-departmental collaboration, strategic planningTeam management, project-focused, collaborative with data teams

The Vp Data Science typically holds a strategic, leadership role overseeing multiple teams and setting long-term data initiatives, while a Data Science Manager focuses on managing data projects and teams directly involved in execution. Both roles require strong technical backgrounds, but the Vp is more involved in high-level planning and organizational strategy.

What are the most commonly searched types of Data Science jobs in Tennessee? The most popular types of Data Science jobs in Tennessee are:
What are popular job titles related to Vp Data Science jobs in Tennessee? For Vp Data Science jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Vp Data Science jobs in Tennessee look for? The top searched job categories for Vp Data Science jobs in Tennessee are:
Vp Data Science jobs near you
Infographic showing various Vp Data Science job openings in Tennessee as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $129,299 per year, or $62.2 per hour.

Vice President of AI

NxT Level

Nashville, TN • On-site

Full-time

Re-posted 8 days ago

Job description

Vice President of AI Strategy & Transformation
Location: Flexible (Hybrid/Remote Options Available) | Industry: Financial Services / Technology / Enterprise AI
We are partnering with a forward-looking organization seeking a Vice President of AI Strategy & Transformation to lead enterprise-wide artificial intelligence initiatives. This executive-level position will drive the development and adoption of AI-powered solutions across business functions, focusing on automation, efficiency, and long-term profitability.
The ideal candidate blends strategic foresight with hands-on execution and brings a deep understanding of both commercial AI tools and internal application development. You will work across departments to identify impactful use cases, build scalable AI capabilities, and cultivate a culture of innovation and data-driven decision-making.
Key Responsibilities
AI Strategy & Roadmap
  • Define and champion the company's enterprise AI strategy in alignment with business objectives
  • Translate pain points and opportunities into actionable AI initiatives with measurable ROI
  • Develop and manage a scalable, multi-year roadmap for AI platform adoption and maturity
?? Tool Development & Execution
  • Build or configure AI-powered applications using tools such as OpenAI, Copilot, or other low-code/no-code platforms
  • Develop intelligent assistants, workflow automations, and internal data agents to streamline business processes
  • Oversee implementation quality, compliance, and lifecycle management of AI tools
Use Case Identification & Vendor Management
  • Collaborate with leaders in operations, marketing, finance, and client servicing to identify high-impact AI applications
  • Evaluate and manage external AI vendors and consulting partners where appropriate
  • Drive business case development and manage RFPs and integrations for outsourced AI tools
Technology & Governance Alignment
  • Coordinate with IT to ensure AI tools align with existing tech stack, architecture, and security protocols
  • Uphold regulatory, privacy, and data governance requirements in all AI deployments
  • Serve as a thought leader in AI risk management, bias mitigation, and responsible AI practices
AI Enablement & Culture
  • Build foundational AI literacy across departments through training and enablement sessions
  • Champion ethical and inclusive AI practices while encouraging experimentation and innovation
  • Serve as a visible change leader helping drive adoption and excitement across the organization

Critical Success Factors
  • Strategic thinker who can connect emerging AI capabilities to tangible business outcomes
  • Hands-on operator with a bias toward action, rapid prototyping, and continuous iteration
  • Adept at translating complex technologies into simple, persuasive business narratives
  • Collaborative leader able to work across business units and influence executive stakeholders
  • Strong advocate for responsible AI principles and regulatory compliance

Key Performance Indicators (KPIs)
  • Advancement in enterprise AI maturity (e.g., Level 1 → Level 3) within 12-24 months
  • Deployment of AI tools that generate measurable cost savings, margin improvements, or process efficiencies
  • Adoption and usage metrics for internal AI tools across departments
  • Completion and effectiveness of company-wide AI training initiatives
  • ROI analysis tied to top-priority AI use cases

Qualifications
  • Bachelor's degree in Marketing, Finance, Business, Engineering, or a related field (Master's preferred)
  • 10+ years of experience in business strategy, digital transformation, or technology leadership
  • 3+ years of hands-on experience managing or deploying AI/ML solutions
  • Proven success in building custom AI applications or managing third-party AI tools
  • Strong grasp of prompt engineering, LLM integration, AI governance, and data infrastructure
  • Experience in regulated industries such as financial services, fintech, healthcare, or insurance is strongly preferred

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