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Chemical Engineering Data Science Jobs (NOW HIRING)

QRC Group

Sr Data Scientist

Juncos, PR · On-site

$110 - $150/hr

... Science, Chemical Engineering, Biomedical Engineering, Biotechnology, Manufacturing Engineering, or a related technical discipline) #J-18808-Ljbffr

Suterra

Chemical Engineering Manager

Bend, OR

Company Description Chemical Engineering Manager Onsite | Bend, OR Suterra is the world's leading ... Analyzing trial data to identify issues and propose improvements. * Define material specifications ...

Validation & Engineering Group, Inc

PR

... Chemical, Food, and Medical Devices industries in the following areas: Laboratory, Compliance ... engineering, computer science or related field experience OR * Bachelors + 4 years of data science ...

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Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ...

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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 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.

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.

Can a chemical engineering data scientist become a data scientist?

A chemical engineering data scientist can transition to a general data scientist role by developing skills in programming, statistical analysis, and machine learning. Their background in chemical processes and data analysis can be valuable, but they may need to gain experience with broader data tools and techniques used across industries. Certifications or training in data science are often helpful for this career shift.
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Infographic showing various Chemical Engineering Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Director of Data Engineering & Data Science - IAA

The Job Sauce

Chicago, IL • On-site

Full-time

Re-posted 16 days ago

Job description

About the Role
IAA is seeking a Director of Data Engineering & Data Science to lead a highly visible, business-critical function at the intersection of data, analytics, machine learning, and business transformation. This leader will define and drive the vision, architecture, and execution for IAA's data engineering and data science capabilities, ensuring the organization can scale advanced analytics, BI, forecasting, machine learning, and AI solutions that directly support business growth and operational excellence.
This role requires a strong technical leader and business problem solver who can partner across a broad set of stakeholders including Operations, Business, Sales, Marketing, Product, and Engineering. The ideal candidate brings deep expertise in the Azure BI and data ecosystem, strong people leadership, and the ability to translate complex business needs into practical, scalable data and AI solutions.
This position reports directly to the VP of Engineering and is a critical, high-visibility leadership role within the organization.
What You'll Do
  • Lead the Data Engineering and Data Science Engineering function for IAA, setting technical vision, delivery strategy, and operating rhythm
  • Build and evolve scalable data platforms, BI architecture, and ML-enablement capabilities using the Azure data and analytics stack
  • Drive strategy and execution across Microsoft Fabric, Synapse, Power BI, Azure BI technologies, and modern cloud data platforms
  • Partner with business and functional leaders to solve high-value problems across Operations, Sales, Marketing, Product, and other key areas
  • Guide the design and implementation of robust pipelines, semantic models, dashboards, self-service analytics, forecasting solutions, and machine learning systems
  • Help shape the roadmap for advanced analytics, predictive modeling, experimentation, and AI-driven insights
  • Mentor, coach, and grow data engineering and data science talent while raising the technical bar across the team
  • Establish strong engineering practices across architecture, delivery quality, scalability, governance, and operational excellence
  • Collaborate closely with engineering leaders and cross-functional teams to ensure data and AI solutions are aligned with platform, product, and business priorities
  • Act as a senior thought partner to leadership on data strategy, technical tradeoffs, and investment priorities

What We're Looking For
  • Proven experience leading Data Engineering, BI, Analytics, and/or Data Science Engineering teams at the Director level or equivalent
  • Deep expertise in the Azure BI / data technology stack, including:
    • Microsoft Fabric
    • Azure Synapse Analytics
    • Power BI
    • Broader Azure data and analytics services
  • Strong understanding of data engineering architecture, modern analytics platforms, and scalable data pipelines
  • Strong foundation in data science, machine learning, and model operationalization
  • Demonstrated ability to solve complex business problems through data, analytics, and technical leadership
  • Strong mentoring, coaching, and people leadership skills with experience growing high-performing technical teams
  • Excellent communication and stakeholder management skills; able to work effectively with a wide range of technical and non-technical partners such as Ops, Business, Sales, Marketing, Product, Engineering
  • Ability to operate successfully in a fast-paced, high-visibility environment with multiple priorities and stakeholders
  • Strong executive presence and the ability to connect technical decisions to business outcomes

Preferred Experience
  • Experience supporting enterprise use cases across operations, commercial functions, and product-driven organizations
  • Experience driving both BI modernization and data science / ML adoption within the same organization
  • Familiarity with cloud-native engineering practices, production-grade data platforms, and secure, scalable AI/ML environments
  • Experience leading organizations that combine data engineering, analytics engineering, BI, and data scienceunder one leadership model

IAA Data Science / Engineering Technology Environment
We are looking for a leader who can guide and expand a modern data and AI ecosystem. Relevant technologies include Azure BI capabilities as well as IAA's broader data science and ML toolset, including technologies such as Python, SQL, Azure Event Hub, Apache Airflow, Synapse, Fabric, Docker, Terraform, DBT, PyTorch, TensorFlow, Vertex AI, Gemini, GPT, Prophet, TBATS, SARIMAX, scikit-learn, CI/CD pipelines, and Azure cloud platform.
Why This Role Matters
This is a critical leadership role for IAA. The Director will help shape how the company uses data, analytics, BI, and AI to make better decisions, improve business performance, unlock operational efficiencies, and create scalable competitive advantage. This leader will influence both technical direction and business outcomes across the organization.

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