Process Data Scientist
Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Sep 24, 2026.
Holcim is the leading partner for sustainable construction with net sales of CHF 15.7 billion in 2025, creating value across the built environment from infrastructure and industry to buildings. Headquartered in Zug, Switzerland, Holcim has more than 50 000 employees in 45 countries - across Europe, Latin America and Asia, Middle East & Africa - and has been recognized as a Global Top Employer by the Top Employers Institute. Holcim offers high-value end-to-end Building Materials and Building Solutions, from foundation and flooring to walling and roofing - powered by premium brands including ECOPact, ECOPlanet, ECOCycle, and Ytong.
Role Overview
As we continue to look for more sustainable ways to build, we need world-class talent to join our team. People who are passionate about sustainability, driven by curiosity, and keen to grow, learn, develop, and thrive in our high-performance culture.
We are looking for an innovative ProcessData Scientist, motivated to solve the bigger operational and energy challenges in our industry. Inspired to develop and deploy cutting-edge AI applications that enhance a new era of kiln and thermal efficiency, our Process Data Scientist is passionate about leveraging digital tools to optimize process stability, reduce fuel consumption, drive Alternative Fuel substitution, and curb CO2 emissions across our Plants of Tomorrow.
You will work on the algorithm design, Machine Learning development and deployment, and implementation of various manufacturing process optimisation initiatives including our flagship solution P-PREDICT.
You will also make sure these solutions integrate seamlessly our legacy and new Plant of Tomorrow technologies to realize our vision for sustainable cement manufacturing.
Main Responsibilities
Machine Learning & Physics-AI Hybrid Model Design, Tuning, and Validation:
Design, configure, and validate hybrid Machine Learning algorithms combining 1st-principles process physics and adaptive AI loops for calciner and kiln optimization.
Develop predictive soft sensors for real-time process variables, like fuel calorific value forecasting of alternative fuels and clinker quality predicting.
Build multi-variable target recommenders for control loops of our kilns and other equipment
Use deep learning, time-series forecasting, and probabilistic models to predict operational hazards (such as ring formation risk, cyclone blockages, and pressure spikes).
Model Lifecycle Management:
Responsible for the end-to-end lifecycle of models (including feature engineering, design, cloud/edge deployment, continuous tuning, and automated retraining pipelines).
Work with our vendors on the deployment and customization of their solutions as well as on development of Holcim proprietary solutions.
Explainable AI (XAI) & Operator Interface Integration:
Design operational logic, reason codes, and metrics to be displayed on operator dashboards to ensure high explainability and user trust.
Collaborate closely with process engineers and plant operators to translate operational knowledge into accurate model boundaries.
Field Adoption & Knowledge Transfer:
Drive global uptake and utilization of P-PREDICT and other PoT initiatives across the world, through structured plant onboarding, commissioning support, and feedback loops.
Provide coaching, knowhow transfer, and technical documentation for plant process engineers.
Your Profile
Level of education/qualifications normally required:
MS or PhD in Computer Science, Chemical Engineering, Data Science, Electrical Engineering, Systems & Control, or equivalent fields.
C3.ai Certifications (C3.ai Data Science, C3.ai V8 Data Science/Application Development) or equivalent enterprise AI framework certification (Advantage).
Experience
3+ years’ experience in applied Machine Learning and Data Science projects focused on heavy industry process optimization (cement, chemical, energy, or mineral processing preferred).
2+ years’ experience productizing and scaling ML models in production environments (both Cloud and EDGE).
2+ years’ experience in industrial change management, coaching, and technical knowledge transfer to guarantee full plant adoption of digital control systems (Advantage).
Technical Competencies
Applied Machine Learning expertise: Anomaly detection, time-series forecasting, regression analysis, probabilistic modeling, supervised classification, and unsupervised learning.
Strong mathematical & domain background: Linear algebra, calculus, probability/statistics, heat and mass transfer, and basic process control dynamics (PID/MPC).
Programming & ML Stack: High proficiency in Python (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy) and R/SQL; experience with prototype and production data pipelines.
Data Warehousing, Data Lakes & Scalable Data Systems: Practical experience working with modern data platforms, including BigQuery, cloud data warehouses, data lakes, and lakehouse architectures on platforms such as GCP or AWS. Ability to efficiently query, transform, integrate, and prepare large-scale datasets for analytics and machine learning. Experience with distributed data processing and scalable ML platforms is a strong plus.
Time-Series & Industrial Data Platforms: Experience working with time-series databases and industrial data platforms such as InfluxDB and Seeq. Familiarity with integrating sensor, process, operational, and contextual data from multiple sources is desirable.
Data Engineering & Pipelines: Experience designing and working with ETL/ELT pipelines, data ingestion, data transformation, feature engineering, data quality, and production data workflows. Understanding of batch and streaming data architectures is a plus.
Data Visualization & Explainable AI: Hands-on experience with Jupyter, Grafana, Looker studio,Seeq, Seaborn, and other visualization tools. Experience building explainable AI solutions, including model interpretation, feature importance, anomaly explanations, and reason-code displays for technical and business users.
Cloud & Production ML: Experience deploying and operating machine-learning solutions in cloud environments. Familiarity with MLOps, model deployment, monitoring, version control, CI/CD, and containerization is a plus.
Benefits
Hybrid work contract
Family Support: Subsidized child care and paid maternity/paternity leave
Professional Growth: Company-sponsored training and professional development.
Discounts and Perks: Subsidized meals, exclusive discounts, and company gifts for special occasions.
Long-service awards
Employee referral bonus
Pension and Savings: Access to a comprehensive pension fund and an employee savings scheme.
Holcim is committed to a diverse and inclusive workplace. We support equal opportunities for everyone, regardless of race, national origin, gender, sexuality, disability, or age.
Important Notice: Holcim recruiters only contact candidates via official @[holcim.com] email addresses. We do not use Gmail or any other non-Holcim domains to conduct recruitment processes. Please verify the sender address before responding."