Research Data Scientist – Materials & AI – R&D Filler Metals

ESAB AB; Göteborgs kommun; On-site; SEK 500,000 - 700,000; Full time

Benefits offered by this job

Professional development opportunities

Dynamic research environment

International collaboration opportunities

Job summary

ESAB AB in Gothenburg is looking for a Research Data Scientist focused on materials and AI to join their R&D Filler Metals team. The role includes developing data architecture, implementing advanced data management tools, and performing machine learning and statistical modeling to enhance product innovation.

Ideal candidates will have a postgraduate degree in Data Science or a similar field, experience in AI, and expertise in Python and SQL. They should be passionate about driving research advancements in welding technology.

Qualifications

• Master's or PhD required in related fields.
• Experience in machine learning and statistical modeling.
• Proficiency in Python and SQL.

Responsibilities

• Develop and manage data architecture for R&D.
• Implement data management tools for quality and accessibility.
• Conduct machine learning for material development and optimization.

Skills

Machine learning

Statistical modeling

Python (pandas, NumPy, scikit-learn)

Data visualization (Matplotlib, Plotly)

Data engineering

Computer vision

Education

M.Sc. or PhD in Data Science, Computer Science, Statistics or related field

Tools

SQL

PyTorch

TensorFlow

Job description

Job Overview

Research Data Scientist – Materials & AI – R&D Filler Metals
Location: Gothenburg, Sweden

ESAB is a global leader in welding and cutting technologies, with over 100 years of innovation behind us. Within our Global Filler Metals organization, the R&D Filler Metals department plays a central role in shaping the future of welding materials worldwide. Our Research Team drives advanced materials research, method and tool development, and deep metallurgical expertise to support breakthrough product innovation.

Responsibilities

• Own and develop the data architecture for Filler Metals R&D, including database design, pipelines, and integration with laboratory systems (LIMS), ensuring data is structured, traceable, and analytics-ready across the full research workflow.
• Evaluate and implement modern data management tools and platforms to continuously improve data quality, accessibility, and reproducibility.
• Develop and deploy ML and statistical models to support material development, process optimization, and quality prediction.
• Lead all AI/ML-based image analysis activities within Filler Metals R&D, including microstructure quantification, defect detection, and weld quality assessment.
• Apply Design of Experiments (DoE) and advanced statistical methods to plan, analyze, and interpret research data efficiently.
• Drive the development of AI‑assisted knowledge retrieval systems, including RAG pipelines and knowledge graphs, leveraging LLM platforms such as Claude, to unlock value from historical R&D reports and technical documentation.
• Act as the AI lead for Filler Metals, coordinating with company‑wide AI initiatives and contributing to the broader ESAB AI community.
• Provide data science support to metallurgical and materials research projects, translating experimental data into actionable insights.
• Participate in selected external research collaborations (e.g., with universities or research institutes) where data science expertise adds value.
• Occasionally support lab operations to understand measurement workflows and ensure data quality at the source.
• Communicate model outputs and analytical results clearly and concisely to both technical and non‑technical audiences.

Qualifications

• M.Sc. or PhD in Data Science, Computer Science, Statistics, Metallurgy, Materials Physics, or a closely related field.
• Demonstrated experience in machine learning and statistical modeling.
• Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow or similar) and SQL.
• Experience with data engineering: building pipelines, managing databases, and handling structured/unstructured experimental data.
• Experience with computer vision and image analysis (classical methods and/or deep learning).
• Solid understanding of statistical analysis and DoE methodology.
• Familiarity with LLM applications, RAG frameworks, or knowledge graph development.
• Strong skills in data visualization and dashboarding (e.g., Matplotlib, Plotly, Power BI or similar).
• Ability to communicate complex analytical results to diverse audiences, both technical peers and business stakeholders.
• Strong problem‑solving skills, structured thinking, and an innovative, open‑minded mindset.
• Proficiency in written and spoken English.

Merits (Advantageous)

• Background or working knowledge in materials science, metallurgy, or welding; understanding the physical meaning behind the data is a significant merit.
• Experience in AI strategy, AI adoption, or acting as an AI champion within an organization.
• Familiarity with LIMS or other laboratory information systems.Experience working in an industrial R&D environment.Knowledge of Swedish.

What we offer

• A dynamic and highly qualified research environment with daily interaction among scientists, engineers, welders, chemists, and technicians.
• The opportunity to work with cutting‑edge technology and contribute to global, long‑term research projects.
• A key role in a team shaping the future of welding metallurgy and sustainable material development.
• Professional development, scientific growth, and international collaboration with laboratories, universities, research bodies, and technology partners worldwide.