Senior Scientist - Data Science & AI, Industrial Process Analytics

Are you excited to apply modern data science to real production plants, help process engineers act faster, smarter, and deliver measurable sustainability impact? Join our Industrial Process Analytics team to build advanced analytics that optimize chemical and biochemical manufacturing at scale.

Key responsibilities

• Design, build, and ship scalable data science applications for manufacturing, from first idea to validated, deployed product.
• Implement model- and data-driven approaches that support plant engineers and operators in day-to-day decisions.
• Collaborate in multi-disciplinary teams including data scientists, process and chemical engineers, cloud and data engineers, reliability and production experts to improve yield, throughput, quality, reliability, and sustainability.
• Generate insights and convert them into measurable impact.
• Champion a data-driven mindset across sites and communicate results clearly to technical and non-technical stakeholders.
• Contribute to best practices for data analysis, coding, and MLOps.
• Stay up to date with state-of-the-art methods and scout and implement new technology in-house.

We offer

• Direct impact on manufacturing operations and sustainability KPIs.
• A global Advanced Analytics team focused on optimizing biotech and chemical processes, fully aligned with dsm-firmenich’s manufacturing excellence strategy.
• Real-world challenges and exposure to all businesses, with the opportunity to contribute strongly to both manufacturing operations as well as S&R up- and downscaling.
• An experienced, supportive community that has optimized plants worldwide, with room to grow your expertise in both ML and manufacturing.

You bring

• PhD or similar experience in (Bio)Chemical Engineering or a related field, or Data Science, Statistics, or Computer Science.
• 3–7 years of additional academic or industrial work experience in manufacturing data science, ideally for (bio)chemical processes.
• Strong hands-on Machine Learning expertise and rigorous, real-world validation.
• Modeling experience for industrial settings, with the ability to dive into methods and details while making pragmatic calls in a results-driven environment.
• Excellent communication and domain translation skills, partnering with engineers, operators, and leadership across complex stakeholder landscapes.
• Excellent problem-solving skills and proven ability to work both independently and collaboratively.
• Ability to create end-to-end computational workflows, from data ingestion to deployment and monitoring.
• Understanding of (bio)chemical processes and process control.
• Experience processing and modeling time-series, tabular, and panel/longitudinal/multi-way data, with exposure to multivariate process analytics and chemometrics. Experience with MPC or system identification is a strong advantage.
• Physics-based and hybrid modeling such as gray-box models, surrogate models, PINNs, and digital twins is a strong plus.
• Familiarity with vision and text-based GenAI for operator guidance, documentation mining, inspection, and related use cases is a plus.

Technical skills

Advanced Analytics and Machine Learning

• Python and core data science stack for data manipulation, visualization, statistics, ML/DL, and time-series/forecasting.
• Multivariate modeling and chemometrics for process monitoring and root-cause analysis.
• Model interpretability and uncertainty.

Software engineering & lifecycle

• Software engineering best practices including git, code review, linting/formatting, unit and integration tests (pytest), packaging (uv), containers (Docker), and exposure to CI/CD.
• Familiarity with data engineering and model management tools such as DBT, databases, and MLFlow.

Nice to have

• Process analytics with Seeq or TrendMiner.
• Causal and robust modeling including DoE/experiment design, Bayesian methods, causal inference, and drift detection.
• Hybrid and control-aware modeling including physics-informed or gray-box models, surrogates for optimization, and MPC integration.
• GenAI, LLMOps, agentic AI, and Vision Language Models.
• Cloud platforms such as AWS, Azure, and Databricks.
• Online learning, IoT and edge scenarios, streaming, and real-time systems.
• Workflows such as Nextflow/CWL or alternatives for reproducible pipelines, including Cora cloud pipeline.
• API and webapp development using FastAPI, Flask, Django, Streamlit, or JavaScript.

About dsm-firmenich

dsm-firmenich is a global team powered by science, creativity, and a shared purpose: to bring progress to life. The company offers opportunities for learning, growth, and mobility across businesses, teams, and borders, in an environment where employees’ voices and ideas matter.