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Chemical Engineering Data Science Jobs in Ohio (NOW HIRING)

Qnity

Senior Data Scientist

Delaware, OH

PhD in Chemical Engineering or Material Science; or MA with +5 industrial experience * Coding skills in R or Python including data preparation, regression, and plotting * Some formal education in ...

Battelle

Chemical Engineer

Columbus, OH · On-site

The selected candidate will be expected to conduct laboratory experimentation, data compilation and ... A Work Environment Where You Succeed For brilliant minds in science, technology, engineering and ...

JM Eagle

Chemical Engineer

Ashtabula, OH · On-site

S.) or equivalent in Chemical Engineering, material science or chemistry from a four-year college ... Designs and plans measurement and control systems for chemical plants based on data collected in ...

PLASTPRO 2000 INC

Chemical Engineer

Ashtabula, OH · On-site

S.) or equivalent in Chemical Engineering, material science or chemistry from a four-year college ... Designs and plans measurement and control systems for chemical plants based on data collected in ...

REalloys Inc.

Sr. Chemical Engineer

Euclid, OH · On-site

$80 - $100/hr

Collaborate with process engineers, metallurgists, and materials scientists to ensure seamless integration of chemical processes into production flows. * Drive continuous improvements in process ...

Fuseki

$79.89 - $108.42/hr

In interdisziplinären Teams, die sich aus den Kompetenzbereichen Software Engineering, Data Science, UX und Projektmanagement zusammensetzen, gestalten wir gemeinsam die Zukunft! Für diese Position ...

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Chemical Engineering Data Science information

What is a chemical engineering data science?

A Chemical Engineering Data Science job combines chemical engineering principles with data science techniques to analyze and optimize chemical processes. Professionals in this field work with large datasets, machine learning models, and statistical methods to improve efficiency, reduce costs, and enhance safety in industries such as pharmaceuticals, energy, and materials. They may develop predictive models, conduct simulations, and implement AI-driven solutions to solve complex engineering challenges. This role requires expertise in programming, data analytics, and chemical process understanding to drive data-informed decision-making.

What does a chemical engineering data science do?

Professionals in Chemical Engineering Data Science typically spend their days collecting and cleaning process data, developing data models to predict or optimize chemical operations, and interpreting analytical results to improve production efficiency or product quality. They often use specialized software to simulate chemical processes and collaborate closely with engineers, plant operators, and IT professionals to implement data-driven solutions. Regular tasks may also include creating reports and data visualizations, troubleshooting data quality issues, and supporting digital transformation projects within manufacturing environments. The role is dynamic and requires continual learning as new tools and methodologies emerge, making strong communication skills and adaptability especially important.

What are the key skills and qualifications needed to thrive in chemical engineering data science?

To succeed in Chemical Engineering Data Science, you need a strong background in chemical engineering principles, statistical analysis, and programming (usually with Python, R, or MATLAB), often supported by a degree in chemical engineering or data science. Familiarity with machine learning algorithms, process simulation software (like Aspen Plus or HYSYS), and data visualization tools is highly valuable, and certifications in data analytics or Six Sigma can be advantageous. Strong analytical thinking, problem-solving, and effective communication skills help you interpret data-driven insights and collaborate with multidisciplinary teams. These competencies are essential for solving complex engineering problems, optimizing processes, and delivering actionable results in data-intensive chemical industry settings.

Can a chemical engineering data scientist become a data scientist?

A chemical engineering data scientist can become a data scientist by expanding their skills in programming, statistics, and machine learning, which are essential for general data science roles. Their domain expertise can be an advantage in industries like energy, pharmaceuticals, or manufacturing, but they may need additional training or certifications in data science tools such as Python, R, or SQL. Transitioning often involves gaining experience with broader data analysis techniques and project management skills common in data science positions.

What are the most commonly searched types of Chemical Engineering Data Science jobs in Ohio?

The most popular types of Chemical Engineering Data Science jobs in Ohio are:

What are popular job titles related to Chemical Engineering Data Science jobs in Ohio?

For Chemical Engineering Data Science jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Chemical Engineering Data Science jobs in Ohio look for?

The top searched job categories for Chemical Engineering Data Science jobs in Ohio are:

Chemical Engineering Data Science jobs near you
Infographic showing various Chemical Engineering Data Science job openings in Ohio as of August 2026, with employment types broken down into 74% Full Time, 10% Part Time, and 16% Contract. Highlights an 87% In-person, 4% Hybrid, and 9% Remote job distribution.

BN31M1-Manager, Scientific AI Engineering & Data Science

Columbus, OH • On-site, Remote

Chemical Abstracts Service
See more jobs at Chemical Abstracts Serviceexternal

Full-time

Posted 9 days ago

Job description

Position Overview
The Manager, Scientific AI Engineering & Data Science is a people-leadership role. The manager builds, grows, and leads a team of data scientists and AI engineers who develop the systems behind CAS's scientific discovery products - the retrieval, extraction, and reasoning that power CAS Newton℠ and CAS Connections, and internal platforms. The role sits in the Data Analytics & Insights (DAI) organization.
The manager's primary work is people: hiring, coaching, developing, and retaining data scientists and AI engineers, and creating the conditions for the team to do its best work. Technical direction, architecture, and roadmap delivery are owned by technical leads and product partners. The manager is expected to carry enough technical fluency to lead, coach, and mentor credibly - to understand the work, judge the quality of an engineer's contributions, and guide growth - but is not accountable for owning the technical roadmap or shipping it.
The role is not bounded by the manager's own set of direct reports. As DAI scales and elevates aggressively, the manager brings team-wide and enterprise-wide thinking to the role and steps in to drive cross-cutting initiatives as priorities dictate. The ideal candidate can speak fluently and confidently about the team's work to both internal and external audiences.
People Leadership & Talent
  • Own hiring for a growing team - sourcing, recruiting, interviewing, and evaluating talent.
  • Develop, retain, and motivate data scientists and AI engineers with scientific domain depth; shape and build the team.
  • Coach and mentor across levels, supporting both technical growth and career progression.
  • Manage performance and career development in line with the DAI career framework - job family, scope tier, and depth/breadth path.
  • Build bench strength, support succession, and sustain a healthy, inclusive, high-expectation team culture.
  • Match people to work thoughtfully, balancing team delivery with individual growth and job satisfaction.
Technical Fluency & Coaching
  • Maintain enough fluency across modern AI engineering - LLMs, agentic workflows and tool use, RAG, retrieval and extraction over scientific content, and evaluation - to lead and coach the team credibly.
  • Judge the quality of the team's technical work well enough to give meaningful feedback and guide development.
  • Understand the trustworthy-AI principles the team works to - including CAS's reliance on curated, provenanced scientific content, and the difference between acceptable model variability and genuine failure - well enough to reinforce them.
  • Partner with technical leads and product, who own technical direction, architecture, and roadmap.
Team Health & Enablement
  • Ensure the team is well-resourced, unblocked, and set up to succeed, working with technical leads and product on prioritization and staffing.
  • Remove organizational and people-level obstacles, and escalate and resolve issues that slow the team.
  • Support healthy operating practices - delivery rhythm, review, and production health - without owning roadmap outcomes.
Team-Wide Leadership & Enterprise Mindset
  • Bring team-wide and enterprise-wide thinking to the role, in service of scaling and elevating the organization aggressively.
  • Step in to lead and drive cross-cutting initiatives as priorities dictate - for example, specific programs with internal partners or targeted team-elevation efforts - unconstrained by the manager's own set of direct reports.
  • Speak fluently and confidently about the team's work to both internal and external audiences, including customers, partners, and the broader scientific community.
  • Approach the role with an ownership mindset that extends beyond the immediate team to the broader organization's success.
Partnership & Communication
  • Partner across Product, Technology, Content Operations, and other teams as the people leader for the team.
  • Represent the team's capacity, needs, and health to stakeholders and leadership.
  • Connect the team's people and capabilities to CAS's broader goals.
Qualifications
Education
  • Master's degree in a relevant technical or quantitative discipline (e.g., Computer Science, Applied Mathematics, Statistics, Data Science, Computational Chemistry, Physics, Bioinformatics), or equivalent experience.
  • A PhD and/or deep scientific domain expertise (chemistry, life sciences, materials science) is valued as a capability the person brings, and is not required.

Experience
  • 8+ years of relevant experience, including 3-5+ years developing people and leading technical teams.
  • Enough hands-on background in AI/ML engineering and data science to lead and coach the work credibly; direct roadmap or delivery ownership is not required at this level.
  • Familiarity with modern AI engineering - LLM-based, agentic, and large-scale retrieval and extraction systems.
  • Experience in scientific, chemical, pharmaceutical, or materials-science domains is desired.

Leadership & Competencies
  • Proven ability to hire, coach, grow, and retain technical talent.
  • Strong people-management, feedback, and career-development skills.
  • Team-wide and enterprise-wide perspective, and readiness to lead initiatives beyond one's own reporting line.
  • Ability to represent the team's work fluently and confidently to internal and external audiences.
  • Sound judgment on team health, culture, and prioritization.
  • Sufficient technical fluency to earn the trust of a team of data scientists and AI engineers.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.

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