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Head Data Science Jobs in Ohio (NOW HIRING)

AssetWatch, Inc.

Head of AI

Columbus, OH

The Head of AI manages AI workstreams across the company, turns scattered AI experiments into ... Lead the AI and Data Science Organization * Build, lead, and develop the team across Machine ...

Bestica Inc.

Data Visualization Specialist

Dayton, OH · On-site

... such as Data Science, Computer Science, Operations Research, or another similar degree ... Head-Up Displays, and other visual tools to support command decisions. Proficiency in business ...

NV5

SAR Scientist

Dayton, OH

We value both heart and head, the diversity of our people, and their experiences because that is ... Data Science). * 6+ years demonstrated experience. * Active Top Secret Clearance with SCI ...

NV5

Spectral Scientist

Dayton, OH

We value both heart and head, the diversity of our people, and their experiences because that is ... Data Science). * 6+ years demonstrated experience. * Active Top Secret Clearance with SCI ...

Xavier University

Data Services Librarian

Cincinnati, OH · On-site

$57K - $60K/yr

Reporting to the Head of Special Services, the enthusiastic and collaborative candidate for this ... Master's degree in Library and Information Science (MLS/MLIS) from an ALA-accredited program

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All JobsHead Data Science JobsHead Data Science Jobs in Ohio

Head Data Science information

See Ohio salary details

$20.6K

$100.7K

$186.5K

How much do head data science jobs pay per year?

As of Jul 26, 2026, the average yearly pay for head data science in Ohio is $100,666.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,847.00 and $136,204.00 per year, depending on experience, location, and employer.

How to become head of data science?

To become a head of data science, professionals typically need extensive experience in data analysis, machine learning, and leadership roles, often requiring 8-10 years in data-related positions. A strong educational background in computer science, statistics, or related fields, along with skills in programming, data management, and strategic planning, is essential. Advanced degrees and certifications in data science or analytics can also enhance prospects for leadership positions.

Is 40 too late for data science?

The Head Data Science role and similar data science positions do not have strict age limits; many professionals transition into data science later in their careers. Success depends on relevant skills, experience, and continuous learning in areas like programming, statistics, and machine learning, regardless of age.

What is the highest paid job in data science?

The highest paid roles in data science are often senior positions such as Chief Data Officer, Director of Data Science, or Lead Data Scientist, with salaries exceeding $150,000 annually and sometimes reaching over $200,000 for those with extensive experience, advanced skills in machine learning, and industry expertise. These roles typically require strong leadership, strategic thinking, and proficiency with tools like Python, R, and cloud platforms.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or features. Data scientists often use this concept to focus on the most impactful variables or tasks to optimize model performance and efficiency.

What does a Head of Data Science do?

A Head of Data Science is responsible for leading and managing the data science team within an organization. They oversee the development and implementation of data-driven strategies, ensuring that the team delivers valuable insights and predictive models to support business goals. This role involves collaborating with other departments, setting the vision for data initiatives, and ensuring best practices in data analysis and machine learning are followed. Additionally, the Head of Data Science often mentors team members and helps shape the organization's overall data strategy.

What are some common challenges faced by a Head of Data Science when building and leading a data science team?

As a Head of Data Science, one of the main challenges is balancing strategic leadership with hands-on technical guidance. You'll often need to align the team's goals with broader business objectives while ensuring that team members have the right mix of skills and resources. Additionally, fostering effective collaboration between data scientists, engineers, and business stakeholders can be complex, especially in cross-functional environments. Managing expectations around project timelines and communicating technical insights in a clear, actionable way are also key aspects of the role.

What are the key skills and qualifications needed to thrive as a Head of Data Science, and why are they important?

To thrive as a Head of Data Science, you need advanced expertise in statistics, machine learning, data modeling, and a strong background in computer science or a related quantitative field, often supported by a master's or Ph.D. Proficiency with programming languages like Python or R, big data platforms such as Hadoop or Spark, and familiarity with cloud-based analytics tools are typically required. Strategic leadership, excellent communication skills, and the ability to mentor and inspire teams are crucial soft skills for this role. These abilities are essential to drive data-driven decision-making, foster innovation, and align analytics initiatives with organizational goals.

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

AspectHead Data ScienceData Science Manager
ResponsibilitiesStrategic leadership, setting data science vision, overseeing multiple teamsTeam management, project delivery, coordinating data science projects
Required SkillsAdvanced analytics, leadership, strategic planningTeam management, technical expertise, project management
ExperienceSenior data science background, leadership rolesData science experience with managerial responsibilities
Work EnvironmentExecutive level, cross-departmental collaborationTeam-focused, project-oriented

The Head Data Science typically holds a strategic, leadership role overseeing the entire data science function, while the Data Science Manager focuses on managing teams and project execution. Both roles require strong technical backgrounds, but the Head Data Science emphasizes vision and strategy, whereas the Data Science Manager concentrates on operational management.

What are the most commonly searched types of Data Science jobs in Ohio? The most popular types of Data Science jobs in Ohio are:
Head Data Science jobs near you
Infographic showing various Head Data Science job openings in Ohio as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $100,666 per year, or $48.4 per hour.

Head of AI

AssetWatch, Inc.

Columbus, OH

Full-time

Retirement, PTO

Posted 13 days ago

Job description

AssetWatch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey.

AssetWatch has a unique opportunity to scale how LLMs, Agents, machine learning, and data science improve customer outcomes, internal productivity, product differentiation, and operational leverage. The Head of AI manages AI workstreams across the company, turns scattered AI experiments into governed and measurable operating capability, and leads the Data Science function.

This is AssetWatch's central strategic leadership role, requiring direct, hands-on involvement. The leader must stay close to the field, understand modern AI and data science deeply enough to scope work directly, and help the company adapt as the technology and vendor ecosystem evolves. Reporting to the CEO, the role demands a blend of strategic vision, technical fluency, ethical leadership, and change management skills.

WHAT YOU WILL DO

Define and Execute AI Strategy

  • Partner with executive leadership to define AssetWatch's AI-native vision, operating model, and continue to build our roadmap heading into 2027 and beyond.
  • Identify where AI can create competitive advantage, drive efficiency, and unlock new customer value, which open new revenue streams.
  • Keep the strategy current as AI capabilities, tooling, and vendor constraints change.

Lead the AI and Data Science Organization

  • Build, lead, and develop the team across Machine Learning Engineering, Machine Learning , and AI Engineering.
  • Recruit, develop, and retain high-performing data scientists, ML engineers, and AI engineers.
  • Establish clear ROI-based goals, accountability, technical standards, and leadership coverage as the team scales.

Run Intake, Prioritization, and Governance

  • Clarify incoming requests by outcome, owner, data dependency, business impact, and build-vs-buy path.
  • Establish guardrails for AI tools, agents, model usage, data access, and acceptable use without slowing down adoption.
  • Set AI Strategy and OKRs in partnership with senior leadership and translate them into measurable team goals with proven ROI.

Advance ML, MLOps, and Applied AI

  • Guide development of physics-based models that improve AssetWatch's reliability intelligence, including anomaly detection, ranking, explainability, and alert quality.
  • Ensure production ML systems are monitored, repeatable, and operationally reliable.
  • Drive AI engineering work including agentic workflows, internal productivity tools, and customer-facing experiences.

Partner Across the Business

  • Work with Product and Engineering to turn AI opportunities into scoped bets with clear owners and delivery paths.
  • Partner with GTM, Customer Success, and Operations to identify high-leverage AI opportunities and improve field workflows.
  • Collaborate with HR, finance, supply chain, and customer support to implement AI-driven automation.

Measure Impact and Communicate Up

  • Define how AI impact is measured and connect AI investments to customer outcomes, efficiency, and revenue.
  • Maintain a clear narrative for the CEO, board, and cross-functional leaders on priorities, progress, and tradeoffs.
  • Evaluate vendors and tooling; recommend when to build, buy, or combine approaches.

WHAT WE ARE LOOKING FOR

Experience

  • 10 or more years leading AI, machine learning, data science, or adjacent data or software\ technical teams.
  • Proven track record setting technological strategy in a fast-moving environment and delivering large-scale initiatives.
  • Experience managing cross-functional teams and partnering with senior executive stakeholders.

Technical Depth

  • Hands-on fluency with modern AI and data science, enough to scope work, evaluate quality, and challenge assumptions.
  • Working knowledge of production ML, MLOps, evaluation, governance, and AI systems lifecycle.
  • Familiarity with state-of-the-art approaches including large language models, agentic architecture, and machine learning.

Business and Leadership Skills

  • Strong judgment connecting technical work to customer value, revenue impact, cost control, and risk reduction.
  • Excellent communicator, able to translate complex AI concepts for non-technical executives and inspire technical teams.
  • Commitment to responsible AI practices including data privacy, bias mitigation, and regulatory compliance.

Education

  • Bachelor's degree in computer science, data science, AI, engineering, or a related field required.
  • Advanced degree (MSc, PhD, or MBA with technology focus) preferred.

NICE TO HAVE

  • Background in industrial technology, predictive maintenance, manufacturing, IoT, or condition monitoring.
  • Experience with time-series data, signal processing, anomaly detection, or sensor-driven products.
  • Experience with AWS, MLOps tooling, cloud data platforms, and enterprise SaaS integrations.

#LI-REMOTE

The base salary range for this full-time position is posted below, plus equity and benefits. Variable pay, bonuses, and other cash compensation will be discussed throughout the interview process.

The salary range was determined by role, level, and location. Individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range applicable to your location during the hiring process.

AssetWatch Salary Range (US)
$244,000—$282,000 USD
What We Offer:
AssetWatch is a remote-first company that puts people at the center of everything we do. We want our team members to thrive - that's why we offer a range of benefits and perks designed to support your well-being, growth, and work-life balance.
  • Competitive compensation package including stock options
  • Flexible work schedule
  • Comprehensive benefits including retirement plan match
  • Opportunity to make a real impact every day
  • Work with a dynamic and growing team
  • Unlimited PTO
We have a distributed team that works remotely across locations in the United States and Ontario, Canada. Collaboration within core working hours is required.