Data Scientist - Materials Characterization, Analytical Sciences & AI Agents

Eurofins•Sunnyvale, CA

6h•$115,000 - $180,000

About The Position

Eurofins EAG Laboratories is seeking a Data Scientist to advance data-driven analysis, intelligent automation, and AI-enabled decision support across materials characterization and analytical testing services. This role applies data science, statistics, machine learning, and AI agent development to complex, multi-modal laboratory data. The Data Scientist will design and deploy AI agents capable of automating data analysis workflows, orchestrating multi-step scientific reasoning, and accelerating insight generation. The role partners with scientists, engineers, and operations to strengthen data quality, automation, and analytics for client testing work. Strong candidates have experience working with materials characterization data (e.g., XPS and other spectral/surface datasets) and understand spectroscopy workflows like peak fitting, quantification, and chemical state analysis.

Requirements

MS or PhD in Materials Science, Data Science, Engineering, Physics, Chemistry, Applied Mathematics, or related field.
Demonstrated experience applying data science, machine learning, or statistical methods in laboratory or engineering environments.
Proficiency in Python and scientific computing libraries (NumPy, Pandas, SciPy, scikit-learn).
Experience working with experimental, image, spectral, sensor, or time-series data.
Experience developing or integrating AI agents, automation frameworks, or LLM-based systems for data analysis or workflow automation (e.g., LangChain, semantic orchestration frameworks, or agent-based architectures).
Strong written and verbal communication skills.
Strong foundation in statistics, applied machine learning, and scientific data analysis.
Ability to evaluate model performance and understand limitations of complex scientific datasets.
Familiarity with ML frameworks such as PyTorch or TensorFlow.
Understanding of AI agent architectures (prompting, tool integration, RAG, multi-agent workflows).
Experience with data visualization and scientific reporting.
Ability to analyze and interpret materials characterization data, including spectral and surface-sensitive datasets (e.g., XPS).
Familiarity with spectroscopy or surface analysis workflows (peak fitting, quantification, chemical state analysis).
Ability to work collaboratively in a client-focused analytical services environment.

Nice To Haves

Experience applying deep learning or representation learning to scientific datasets.
Familiarity with multi-modal data modeling (image, spectral, time-series).
Experience working with surface analysis or spectroscopy datasets (e.g., XPS, AES, SIMS) in a research or industry setting.
Hands-on experience designing AI agents for scientific workflows, including autonomous analysis and decision logic.
Exposure to modern neural architectures (CNNs, transformers).
Knowledge of signal processing, spectral deconvolution, or feature extraction for materials data.
Experience with agent lifecycle practices (evaluation, monitoring, governance).

Responsibilities

Apply data science, machine learning, and AI methods to analyze and interpret materials characterization data across microscopy, spectroscopy, diffraction, and related techniques.
Design, build, and deploy AI agents that automate data analysis tasks, including data ingestion, preprocessing, feature extraction, model selection, and report generation.
Develop and maintain scalable data workflows, pipelines, and agent-based systems that improve efficiency, reproducibility, and throughput of laboratory data processing.
Implement agent-driven orchestration of multi-step analytical workflows, enabling autonomous or semi-autonomous execution of scientific data analysis.
Perform statistical analysis, feature engineering, and exploratory data analysis to identify trends, anomalies, and correlations in experimental datasets.
Build, validate, and document predictive and classification models for materials characterization, failure analysis, and process optimization.
Collaborate with scientists and engineers to translate experimental challenges into AI-augmented and agent-enabled solutions.
Support automation and standardization of data collection, processing, and reporting using both traditional pipelines and intelligent agents.
Communicate analytical insights and AI-driven results clearly to technical and non-technical stakeholders.
Contribute to continuous improvement of data infrastructure, AI capabilities, and advanced analytics platforms.
This position works with companies that deal with defense-related activities and is subject to ITAR (International Traffic in Arms Regulations).
All considered applicants must be U.S. Persons as defined by ITAR: U.S. Citizen, U.S. Permanent Resident (i.e. “Green Card Holder”), or Political Asylee or Refuge.