Data Scientist, Materials Characterization

Company Description

Eurofins EAG Laboratories has almost 50 years of experience in engineering and materials testing services. Our parent company, Eurofins Scientific, is a multi-billion-dollar global leader in engineering and scientific services operating over 950 labs in 60 countries with 65,000 employees with a portfolio of over 200,000 validated analytical methods. 

At EAG, curiosity fuels everything we do. Every analysis, every test, and every question asked in our labs helps unlock what materials and products are capable of—and what the world can build next. If you’re energized by materials science, analytical chemistry, engineering rigor, and solving problems that matter, you’ll feel right at home here.

Every analysis, every test, and every question asked in our labs helps unlock what materials and products are capable of—and what the world can build next. If you’re energized by materials science, analytical chemistry, and engineering rigor, you’ll feel right at home here.

Our scientists and engineers support industries ranging from semiconductors and advanced electronics to pharmaceuticals, medical devices, energy systems, and consumer products. Using advanced techniques in analytical chemistry, microscopy, surface analysis, and engineering sciences, we help clients solve complex challenges across the product lifecycle.

What sets EAG apart isn’t just our world‑class instrumentation—it’s our people. We’re problem solvers, collaborators, and lifelong learners who value precision, curiosity, and impact. Here, your work directly influences the safety, reliability, and performance of products used around the globe.

Imagine the possibilities. Let’s explore them together.

Job Description

Eurofins EAG Laboratories is hiring a Data Scientist to improve scientific data analysis, workflow automation, and AI-supported decision-making across our materials characterization and analytical testing services.

Please note: This is not a general business analytics or software-only data science role. Candidates should have experience applying data science or machine learning to scientific, laboratory, engineering, or experimental data and have had experience operating and performing analysis using materials characterization instrumentation.

This role is best suited for someone who has applied data science in a laboratory, engineering, or scientific environment and can work with complex datasets such as spectral, image, surface, sensor, or time-series data. The Data Scientist will build practical tools and workflows that improve data quality, analysis speed, and reporting in a production lab setting.

What You’ll Do

• Analyze complex scientific data using statistics, machine learning, and data science methods
• Build and improve workflows for data ingestion, cleaning, feature extraction, modeling, and reporting
• Develop AI-enabled tools or workflow automation to support repeatable scientific analysis tasks
• Partner with scientists, engineers, and operations teams to solve practical lab data problems
• Build, test, validate, and document predictive or classification models for scientific applications
• Perform exploratory analysis to identify trends, anomalies, and relationships in experimental data
• Support automation and standardization of data analysis and reporting processes
• Communicate results clearly to both technical and non-technical stakeholders
• Contribute to scalable, reproducible data workflows that improve lab efficiency

Qualifications

Minimum Job Requirements

• A Degree in Data Science or Computer Science AND
• MS or PhD in Materials Science, Engineering, Physics, Chemistry or related field
• Hands-on experience applying data science or machine learning in a scientific, laboratory, manufacturing, or engineering environment
• Strong Python skills (NumPy, Pandas, SciPy, scikit-learn)
• Experience working with experimental, spectral, image, sensor, or time-series data
• Experience analyzing data generated from materials characterization instrumentation
• Experience building data workflows, automation tools, or applied AI solutions
• Strong written and verbal communication skills

Preferred Qualifications

• Experience with materials characterization techniques (e.g., XPS, AES, SIMS, microscopy, spectroscopy)
• Familiarity with spectroscopy workflows (peak fitting, quantification, chemical state analysis)
• Experience with PyTorch, TensorFlow, or similar frameworks
• Experience with multimodal scientific datasets
• Experience developing or integrating AI-enabled workflow tools, including LLM-based systems
• Familiarity with signal processing, spectral analysis, or feature extrac