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Internship Chemical Engineering Data Science Jobs in Ohio

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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 ...

Socket.dev

Data Science & Analytics Specialist

Bath, OH · On-site

$90 - $130/hr

Overview Apogee Engineering has a exciting position for a Data Science & Analytics Specialist that ... internships, or professional work). * Experience using data analysis tools or languages (e.g ...

Posted today

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 ...

Guidehouse

Data Engineer

Beavercreek, OH · On-site

$53K - $88K/yr

Experience in data engineering, software engineering, data analytics, computer science, or a ... Internship, academic, research, or project experience involving data engineering, cloud computing ...

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Internship Chemical Engineering Data Science information

What is an internship in chemical engineering data science?

An internship in Chemical Engineering Data Science is a temporary position for students or recent graduates to gain hands-on experience applying data science techniques to chemical engineering problems. Interns work on projects involving data analysis, modeling, and simulation to optimize processes, improve efficiency, or solve real-world challenges in fields such as energy, pharmaceuticals, or manufacturing. This role provides valuable exposure to both chemical engineering concepts and advanced data science tools, preparing interns for careers at the intersection of these disciplines.

What types of projects can an intern expect to work on in a chemical engineering data science internship?

As a Chemical Engineering Data Science intern, you can expect to work on projects that combine data analysis with chemical process optimization. Typical tasks may include analyzing large datasets from lab experiments or manufacturing systems, developing predictive models for process improvement, and visualizing data to support decision-making. Interns often collaborate closely with chemical engineers, data scientists, and production teams, gaining exposure to both technical and practical aspects of the field. This hands-on experience can provide valuable insights into how data science drives innovation in chemical engineering.

What are the key skills and qualifications needed to thrive as an internship chemical engineering data science?

To excel in an Internship in Chemical Engineering Data Science, you typically need a background in chemical engineering, strong analytical abilities, and foundational knowledge of data analysis and statistics. Familiarity with programming languages such as Python or R, data visualization tools, and simulation software is often required. Effective communication, problem-solving skills, and the ability to collaborate within multidisciplinary teams set top candidates apart. These competencies are crucial for interpreting complex data, developing innovative solutions, and contributing to data-driven decision-making in engineering projects.

What is the difference between Internship Chemical Engineering Data Science vs Internship Chemical Engineering?

AspectInternship Chemical Engineering Data ScienceInternship Chemical Engineering
Required CredentialsRelevant coursework in chemical engineering and data science, possibly some programming skillsBasic chemical engineering knowledge, possibly some engineering coursework
Work EnvironmentData analysis labs, software development environments, research teamsProcess plants, laboratories, manufacturing facilities
Employer & Industry UsageTech companies, consulting firms, R&D departments in chemical industriesChemical plants, manufacturing companies, energy sectors

Internship Chemical Engineering Data Science focuses on applying data analysis and programming skills to chemical engineering problems, often in tech-driven environments. In contrast, Internship Chemical Engineering emphasizes traditional chemical processes and manufacturing. Both roles require foundational chemical engineering knowledge but differ in technical focus and work settings.

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 Internship Chemical Engineering Data Science jobs in Ohio?

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

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

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

Internship Chemical Engineering Data Science jobs near you

BN31M1-Manager, Scientific AI Engineering & Data Science

Chemical Abstracts Service

Columbus, OH • On-site, Remote

Full-time

Posted 5 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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