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Vp Data Science Jobs in Ohio (NOW HIRING)

Premier Health

VP-DATA/ANALYTICS & AI

Dayton, OH

Master's degree (e.g., MBA, MS Data Science, MS Health Informatics) preferred. Licensure ... P Data & Analytics, Chief Analytics Officer, Chief AI Officer) with progressive leadership ...

New

Premier Health

VP-DATA/ANALYTICS & AI

Dayton, OH

Master's degree (e.g., MBA, MS Data Science, MS Health Informatics) preferred. Licensure ... P Data & Analytics, Chief Analytics Officer, Chief AI Officer) with progressive leadership ...

New

Premier Health

VP-DATA/ANALYTICS & AI

Dayton, OH · On-site

Master's degree (e.g., MBA, MS Data Science, MS Health Informatics) preferred. Licensure ... P Data & Analytics, Chief Analytics Officer, Chief AI Officer) with progressive leadership ...

New

Premier Health

VP-DATA/ANALYTICS & AI

Dayton, OH · On-site

Master's degree (e.g., MBA, MS Data Science, MS Health Informatics) preferred. Licensure ... P Data & Analytics, Chief Analytics Officer, Chief AI Officer) with progressive leadership ...

New

Icon plc

We are currently seeking a Vice President, Medical Imaging to join our Medical & Scientific Affairs leadership team within ICON Medical Imaging & Cardiac Safety Services. This executive leadership ...

CBIZ

Vice President | Compensation

Cleveland, OH · On-site

Vice President Of Compensation The Vice President of Compensation will lead the design, governance ... Serve as a trusted advisor to senior leaders on complex compensation matters, providing data driven ...

RELX

Vice President of Government Sales

Dayton, OH

The company employs over 2,000 technologists, data scientists, and experts to develop, test, and ... The Vice President of Government Sales should be a proven sales leader with experience building and ...

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All JobsVp Data Science JobsVp Data Science Jobs in Ohio

Vp Data Science information

See Ohio salary details

$39.5K

$135.4K

$191.1K

How much do vp data science jobs pay per year?

As of Jul 31, 2026, the average yearly pay for vp data science in Ohio is $135,437.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,700.00 and $158,300.00 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, and why are they important?

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 Ohio? The most popular types of Data Science jobs in Ohio are:
What are popular job titles related to Vp Data Science jobs in Ohio? For Vp Data Science jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Vp Data Science jobs in Ohio look for? The top searched job categories for Vp Data Science jobs in Ohio are:
What cities in Ohio are hiring for Vp Data Science jobs? Cities in Ohio with the most Vp Data Science job openings:
Vp Data Science jobs near you
Infographic showing various Vp Data Science job openings in Ohio as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $135,437 per year, or $65.1 per hour.

VP-DATA/ANALYTICS & AI

Premier Health

Dayton, OH

Other

Posted 2 days ago

New

Job description

The VP of Data, Analytics and AI is an executive leadership role responsible for leveraging data, analytics, and artificial intelligence to drive business and clinical value, foster a data-driven culture, and ensure effective governance and use of Premier's data assets and ecosystem. Reporting to the Chief Digital Information Officer (CDIO), the VP of Data, Analytics and AI leads the execution of Premier's enterprise data, analytics, and AI strategy and operating model, builds executive and Board-level trust in data as a strategic asset; operationalizes enterprise data governance in partnership with security, privacy, risk, and compliance leaders; and develops the talent and culture required to mature Premier's data and analytics capability.  This role will partner with senior leaders across Premier Health to understand their data needs and deliver tools to enable them to achieve their business, clinical and operational initiatives.

As a member of the DHT senior leadership team, the VP of Data, Analytics and AI leads day-to-day strategy execution, operating model design, and delivery for the data, analytics, and AI portfolio.  This role will work in close coordination with the CDIO's technology infrastructure, Epic platform governance, and cybersecurity functions to ensure a single, unified digital agenda to support the strategic initiatives of Premier Health.  The VP of Data, Analytics and AI also plays a leading role in building data governance and analytics infrastructure supporting Premier's Wright State University academic partnership and Academic Medical Center designation, consistent with priorities set by the organization.

Education: Bachelor's Degree in Business Administration, Computer Science, Data Science, Information Systems, Health Informatics, or a related field. Master's degree (e.g., MBA, MS Data Science, MS Health Informatics) preferred.

Licensure/Certification/Registration: None required; Certified Analytics Professional (CAP) or comparable data/AI governance certification preferred.

Experience: Minimum 10 to 15 years of progressive business experience, recently at or near the executive level such as Senior data, analytics, or business intelligence executive (e.g., VP/SVP Data & Analytics, Chief Analytics Officer, Chief AI Officer) with progressive leadership experience required. Must have experience with enterprise data & analytics strategy, data governance, AI enablement, and cross-functional program leadership. Preferred experience: Healthcare or health system experience; academic medical center or university-affiliated data governance experience; strategy or management consulting background.
Other experience requirements: 5 or more years of progressive leadership managing cross-functional, multidisciplinary data and analytics teams across a complex organization.

Knowledge/Skills:
      Enterprise data, analytics, and AI strategy development and execution, including policy development
      Data and analytics governance (master data management, data quality, data stewardship) and connected governance with privacy, security, and compliance functions
      Broad understanding of information architectures (data fabric, data mesh, data warehouse, data lake, data hub) and analytics approaches (descriptive, diagnostic, predictive, prescriptive)
      AI fundamentals, including AI-ready data practices, responsible/ethical AI use, and awareness of applicable healthcare regulations (e.g., HIPAA, information blocking rules, emerging state and federal AI regulation)
      Business and financial acumen; ability to translate data, analytics, and AI concepts into business and clinical outcomes and ROI
      Exceptional executive communication, storytelling, and stakeholder influence across clinical, operational, financial, and Board audiences
      Demonstrated leadership building and managing complex, multidisciplinary teams and driving enterprise-wide culture change
      Experience operating within a CIO/CDIO-led digital governance structure, and partnering effectively with CISO, Compliance, and Legal functions on data trust, security, and governance
      Statistical literacy (e.g., understanding the difference between correlation and causation) sufficient to guide analytically rigorous decision-making

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