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Remote Chemical Engineering Data Science Jobs (NOW HIRING)

Weekday AI

Chemical Engineering Expert

$65 - $90/hr

... chemical engineering applications ... This is a flexible, fully remote, part-time opportunity requiring approximately 10 hours per week ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Valdera

Chemical Data Scientist

San Francisco, CA · On-site +1

... science, data engineering, or applied data role, ideally with exposure to messy, real-world or ... moving, remote environment with ambiguous, evolving priorities Salary Range: Salary ranges are ...

Valdera

Chemical Data Scientist

... science, data engineering, or applied data role, ideally with exposure to messy, real-world or ... moving, remote environment with ambiguous, evolving priorities Salary Range: Salary ranges are ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ... Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.) * Experience ...

The Chemours Company

AI & Data Science Intern

Wilmington, Delaware (remote work possible for outstanding candidates, who must be able to ... Establish evaluations & improve accuracy of a GenAI model that is being used to extract chemical ...

The Chemours Company

AI & Data Science Intern

Wilmington, Delaware (remote work possible for outstanding candidates, who must be able to ... Establish evaluations & improve accuracy of a GenAI model that is being used to extract chemical ...

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Remote Chemical Engineering Data Science information

What is a remote chemical engineering data scientist?

A Remote Chemical Engineering Data Scientist is a professional who applies data science techniques, such as machine learning and statistical analysis, to chemical engineering problems while working outside a traditional office setting. They analyze data from chemical processes, develop predictive models, and help optimize production, often collaborating with teams virtually. This role requires a strong foundation in chemical engineering principles, programming skills, and experience with data analytics tools. Working remotely offers flexibility but also demands excellent communication and self-management skills.

What are the key skills and qualifications needed to thrive as a remote chemical engineering data scientist?

To excel as a Remote Chemical Engineering Data Scientist, you need a strong background in chemical engineering principles, data analysis, and statistical modeling, often supported by a degree in engineering or data science. Proficiency in programming languages like Python or R, experience with machine learning frameworks, and familiarity with process simulation tools are typically required. Exceptional problem-solving skills, communication, and the ability to collaborate virtually make candidates stand out in this remote environment. These capabilities are vital for transforming complex chemical process data into actionable insights and driving innovation from a distance.

How do remote chemical engineering data scientists typically collaborate with cross-functional teams?

Remote chemical engineering data scientists often work closely with R&D, process engineering, and IT teams to analyze complex datasets and develop data-driven solutions. Collaboration is facilitated through virtual meetings, shared digital platforms, and clear documentation. Regular communication and project management tools help coordinate tasks, track progress, and ensure that insights are effectively integrated into engineering projects. Building strong relationships remotely can be a challenge, but proactive communication and participation in team discussions are key to successful collaboration.

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

AspectRemote Chemical Engineering Data ScienceRemote Chemical Engineering
Required CredentialsBachelor's or higher in Chemical Engineering, Data Science, or related fields; knowledge of programming and data analysisBachelor's or higher in Chemical Engineering; engineering licensure may be preferred
Work EnvironmentPrimarily remote, involving data analysis, modeling, and software toolsRemote or on-site, focusing on process design, safety, and plant operations
Employer & Industry UsageTech companies, consulting firms, or R&D departments integrating data scienceManufacturing, oil & gas, pharmaceuticals, and chemical plants

Remote Chemical Engineering Data Science combines chemical engineering principles with data analysis skills, often working remotely on modeling and data-driven decision-making. In contrast, Remote Chemical Engineering focuses on process design and plant operations, which may involve on-site work. Both roles require a chemical engineering background but differ in technical focus and work environment.

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

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States with the most job openings for Remote Chemical Engineering Data Science jobs include:

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

Remote Chemical Engineering Data Science jobs near you
Infographic showing various Remote Chemical Engineering Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

BN31M1-Manager, Scientific AI Engineering & Data Science

Chemical Abstracts Service

Columbus, OH • On-site, Remote

Full-time

Posted 21 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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