Senior Data Scientist Manufacturing & Process AI

Role Overview: As a Senior Data Scientist (Manufacturing & Process AI), you will be responsible for building and deploying machine learning models directly on plant data to reduce energy consumption, enhance critical equipment reliability, and improve product quality within the operations. This role is highly collaborative and hands-on, working closely with process, operations, and reliability teams. Your success will be evaluated based on finance-validated operational savings metrics like kcal/kg clinker, kWh/tonne, and avoided downtime. Key Responsibilities: - ML Modeling for Industry: Develop, validate, and implement ML models for process optimization, predictive maintenance on critical equipment, and quality/clinker-factor optimization. - Industrial Data Wrangling: Clean and process high-frequency sensor and time-series data from plant historians, DCS, and IIoT systems to extract meaningful features. - Bridge the Gap (OT to AI): Collaborate with plant operators and process engineers to incorporate domain knowledge into models and transition from advisory recommendations to closed-loop control. - Business & Financial Rigor: Establish robust baselines, quantify financial impact, and defend data results with institutional discipline. - Production Deployment: Work with the MLOps/Platform team to operationalize models and monitor their performance in live operations. Qualification Required: - Education & Experience: - Degree: Bachelor's or Masters degree in Engineering (Chemical, Mechanical, Electrical, Industrial), Statistics, Computer Science, or a related quantitative field. - Experience: 36 years of experience in building and deploying ML models, preferably in a manufacturing or process-industry setting such as cement, steel, refining, chemicals, power, or mining. - Technical Skill Set: - Core Machine Learning: Proficiency in classical machine learning techniques including regularized regression, tree-based ensembles, SVM, k-NN, and Naive Bayes. - Data Science & Engineering: Strong Python skills (NumPy, pandas, SciPy, scikit-learn, statsmodels) and mandatory SQL expertise. - Time-Series & Analytics: Applied skills in time-series analysis, anomaly detection, regression, and forecasting with a solid statistics foundation. - Soft Skills: Ability to communicate complex models effectively to engineers and operators. Note: Additional details about the company were not included in the provided job description. Role Overview: As a Senior Data Scientist (Manufacturing & Process AI), you will be responsible for building and deploying machine learning models directly on plant data to reduce energy consumption, enhance critical equipment reliability, and improve product quality within the operations. This role is highly collaborative and hands-on, working closely with process, operations, and reliability teams. Your success will be evaluated based on finance-validated operational savings metrics like kcal/kg clinker, kWh/tonne, and avoided downtime. Key Responsibilities: - ML Modeling for Industry: Develop, validate, and implement ML models for process optimization, predictive maintenance on critical equipment, and quality/clinker-factor optimization. - Industrial Data Wrangling: Clean and process high-frequency sensor and time-series data from plant historians, DCS, and IIoT systems to extract meaningful features. - Bridge the Gap (OT to AI): Collaborate with plant operators and process engineers to incorporate domain knowledge into models and transition from advisory recommendations to closed-loop control. - Business & Financial Rigor: Establish robust baselines, quantify financial impact, and defend data results with institutional discipline. - Production Deployment: Work with the MLOps/Platform team to operationalize models and monitor their performance in live operations. Qualification Required: - Education & Experience: - Degree: Bachelor's or Masters degree in Engineering (Chemical, Mechanical, Electrical, Industrial), Statistics, Computer Science, or a related quantitative field. - Experience: 36 years of experience in building and deploying ML models, preferably in a manufacturing or process-industry setting such as cement, steel, refining, chemicals, power, or mining. - Technical Skill Set: - Core Machine Learning: Proficiency in classical machine learning techniques including regularized regression, tree-based ensembles, SVM, k-NN, and Naive Bayes. - Data Science & Engineering: Strong Python skills (NumPy, pandas, SciPy, scikit-learn, statsmodels) and mandatory SQL expertise. - Time-Series & Analytics: Applied skills in time-series analysis, anomaly detection, regression, and forecasting with a solid statistics foundation. - Soft Skills: Ability to communicate complex models effectively to engineers and operators. Note: Additional details about the company were not included in the provided job description.