Process Data Scientist

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). 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. 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. 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.

Holcim is the leading partner for sustainable construction, creating value across the built environment from infrastructure and industry to buildings. Headquartered in Zug, Switzerland, Holcim has more than 48,000 employees in 45 attractive markets – across Europe, Latin America, Asia, Middle East & Africa. Holcim offers high-value end-to-end Building Materials and Building Solutions, from foundations and flooring to roofing and walling – powered by premium brands including ECOPlanet, ECOPact, and ECOCycle®. As a global leader in innovative and sustainable building solutions, Holcim is enabling greener cities, smarter infrastructure and improving living standards around the world. With sustainability at the core of our strategy, we are becoming a net-zero company, with our people and communities at the heart of our success. We are driving circular construction as a world leader in recycling to build more with less. It’s all thanks to our 70,000 talented people around the world who are passionate about building progress for people and the planet through four business segments: Cement, Ready-Mix Concrete, Aggregates and Solutions & Products.

• 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).

• 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).

• 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).

• High proficiency in Python (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy) and R/SQL; experience with prototype and production data pipelines.

• 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.

• 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.

• 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.

• 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.

• 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.