Sr Data Scientist

Job description

Senior Data Scientist-Contractor Job AI ML Consultant Sr. Data Scientist Manufacturing Process AI Location: Delhi NCR **About the role** You will build and deploy machine-learning models directly on plant data to cut energy consumption, improve equipment reliability, and tighten product quality across our cement operations. This is a hands-on modelling role embedded with process, operations and reliability teams your work will be measured in real, finance-validated savings (kcal/kg clinker, kWh/tonne, avoided downtime), not slide decks. **Key responsibilities** Build, validate and deploy ML models for process optimisation (kiln / pyro-process control, grinding separator efficiency), predictive maintenance on critical rotating equipment, and quality / clinker-factor optimisation. Work with high-frequency sensor and time-series data from plant historians, DCS and IIoT systems; engineer meaningful features from noisy, real-world industrial signals. Partner with plant operators and process engineers to encode domain knowledge into models, and to take models safely from advisory recommendations toward closed-loop control. Establish rigorous baselines and quantify impact with finance-grade discipline; defend results under scrutiny. Work with the MLOps / platform team to productionise models and monitor them in live operation. Communicate findings clearly to non-technical plant leadership. **Required qualifications (must-have)** Bachelor's or Master's in Engineering (Chemical, Mechanical, Electrical, Industrial), Statistics, Computer Science, or a related quantitative field. 36 years building and deploying ML models, including demonstrable experience in a manufacturing or process-industry environment (cement, steel, refining, chemicals, power, glass, mining, or similar). Strong applied skills in time-series analysis, sensor/signal data, anomaly detection, regression and forecasting, with a solid statistics foundation. Strong, idiomatic Python for data science (NumPy, pandas, SciPy, scikit-learn, statsmodels) with clean, tested, production-quality code; strong SQL. Deep command of classical / traditional machine learning regularised regression (Ridge, Lasso, ElasticNet), tree-based ensembles (Random Forest, Gradient Boosting XGBoost / LightGBM / CatBoost), SVM, k-NN and Naive Bayes with sound feature engineering, cross-validation and hyperparameter tuning. Proven ability to wrangle messy industrial data and engineer features that work in production. Comfortable on the plant floor explaining models to engineers and operators and earning their trust. **Preferred (strong pluses)** Hands-on experience with Industrial IoT (IIoT) and Operational Technology (OT) data plant historians (OSIsoft PI / AVEVA, Aspen IP.21), OPC-UA, SCADA / DCS, time-series databases. Domain exposure to cement or heavy/process manufacturing (pyroprocessing, grinding, combustion, quality control). Experience working with data from SAP (ERP especially PM / PP / production maintenance modules) and Salesforce (SFDC). Familiarity with Advanced Process Control (APC) concepts and closed-loop deployment. Deep learning for time series; physics-informed or hybrid (data + first-principles) modelling. **Technical skills** Programming engineering: idiomatic, production-quality Python NumPy, pandas and SciPy for vectorised data work; clean, modular code with unit tests (pytest); OOP; virtual environments packaging; Jupyter; Git. Strong SQL; PySpark for large datasets a plus. Classical machine learning: hands-on depth across regularised regression, tree-based ensembles (Random Forest, XGBoost / LightGBM / CatBoost), SVM, k-NN and Naive Bayes; unsupervised methods k-means, DBSCAN, hierarchical clustering and PCA / dimensionality reduction. Statistical modelling rigour: hypothesis testing, regression diagnostics, feature engineering selection, cross-validation, hyperparameter tuning, class-imbalance handling, and disciplined error analysis. Time-series anomaly detection: classical methods (ARIMA / SARIMA, exponential smoothing, state-space models) and libraries (statsmodels, sktime, tsfresh, Prophet); anomaly detection (Isolation Forest, One-Class SVM). Core libraries: scikit-learn, statsmodels, XGBoost / LightGBM, matplotlib / seaborn. **Platform tooling** Cloud / lakehouse (Azure, AWS or Databricks); plant historian OT connectors; Git-based workflows. r Required. AI ML Consultant AI ML Consultant Experience LevelSenior Level Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.