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 (data scientists, process/chemical engineers, cloud & 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; communicate results with clarity 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, scout and implement new technology in-house
You Bring:
- PhD or similar experience in (Bio)Chemical Engineering or a related field, or Data Science/Statistics/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 for industrial settings: you can dive into methods and details, yet make pragmatic calls in a results‑driven environment.
- Excellent communication and “domain translation” skills—partnering with engineers, operators, and leadership; navigating 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, ideally including:
- Experience processing and modeling time‑series, tabular, and panel/longitudinal/multi‑way data; exposure to multivariate process analytics/chemometrics. Experience with MPC or system identification is a strong advantage.
- Physics‑based and hybrid modeling (e.g., gray‑box, surrogate models, PINNs, digital twins) is a strong plus.
- Familiarity with vision and text‑based GenAI (for operator guidance, documentation mining, inspection, etc.) 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/Chemometrics for process monitoring and root‑cause analysis.
- Model interpretability and uncertainty
Software engineering & lifecycle
- Software engineering best practices: git, code review, linting/formatting, unit/integration tests (pytest), packaging (uv), containers (Docker), exposure to CI/CD.
- Familiarity with data engineering and model management (DBT, databases, MLFlow)
Nice to have
- Process analytics with Seeq or TrendMiner
- Causal & robust modeling: DoE/experiment design, Bayesian methods, causal inference, drift detection
- Hybrid & control‑aware modeling: physics‑informed/gray‑box models, surrogates for optimization, MPC integration.
- GenAI, LLMOps, agentic AI, Vision Language Models
- Cloud platforms, AWS, Azure, Databricks.
- Online learning, IoT and edge scenarios, streaming and real-time
- Workflows: Nextflow/CWL or alternatives for reproducible pipelines, Cora cloud pipeline
- API and webapp development (FastAPI, Flask, Django, streamlit, JavaScript)
