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Remote Chemical Engineering Data Science Jobs in Austin, TX

Meta

Data Engineer, Product Analytics

Austin, TX · On-site +1

$177K/yr

In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user ...

Driver AI Inc.

Applied Data Scientist, LLM Evaluation

Austin, TX · On-site +1

$175K - $275K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Remote or Austin, Tx Our value is directly tied to the quality of our content at scale. The ... Minimum 3 - 5 years in applied science, ML engineering, or data science roles with a focus on ...

Amat

Manager, Data Scientist

Austin, TX · On-site +1

$176K - $242K/yr

If you want to push the boundaries of materials science and engineering to create next generation ... Collaborate with business stakeholders, product teams, engineers, data scientists, and subject ...

Arrive Logistics

Data Scientist II

Austin, TX · On-site +1

... Data Science, Product, and Engineering to build and improve ML and AI systems that drive ... operational value. This role is a great fit for a hands-on practitioner with applied experience in ...

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All JobsChemical Engineering Data Science JobsRemote Chemical Engineering Data Science JobsRemote Chemical Engineering Data Science Jobs in Austin, TX

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.

What are popular job titles related to Remote Chemical Engineering Data Science jobs in Austin, TX?

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

What job categories do people searching Remote Chemical Engineering Data Science jobs in Austin, TX look for?

The top searched job categories for Remote Chemical Engineering Data Science jobs in Austin, TX are:

Remote Chemical Engineering Data Science jobs near you
Infographic showing various Remote Chemical Engineering Data Science job openings in Austin, TX as of August 2026, with employment types broken down into 62% Full Time, 13% Part Time, and 25% Contract. Highlights an 100% Remote job distribution.

Senior Product Manager : Full-time

HARAMAIN SYSTEMS INC.

Austin, TX • On-site, Remote

$119K - $157K/yr

Full-time

Posted 21 days ago

Job description

Job Description
Role: Senior Product Manager (IC, reports to Principal PM) Focus: Internal tooling & big data products; cross-functional with engineering & data science
Role: Senior Product Manager
Location : Remote
Job type : | Full-time
Must-haves:
  • 5+ years PM or founder/technical product role
  • Prior engineering title (SWE, DS, ML Eng, or technical analyst)
  • Startup experience (Series B/C, not big tech)
  • Strong technical background: Python, AWS, data pipelines
  • Big data tools explicitly listed (Spark, Databricks, BigQuery, Snowflake, Redshift)
  • Resume shows internal tooling + big data product work with named projects
  • US-based, authorized to work

Nice-to-haves:
  • Fintech/payments/fraud/identity domain
  • Founder/entrepreneurial mindset

Avoid:
  • Purely customer-facing PMs uninterested in internal work
  • Big tech/large enterprise/bank-only backgrounds
  • Traditional PM path without engineering roots
  • Buzzword-heavy AI/ML profiles without real data platform experience
  • Short tenure (<2 years per role)

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