Contractor - Data Science & AI 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)

• . Bachelors or Masters in Engineering (Chemical, Mechanical, Electrical, Industrial),
• Statistics, Computer Science, or a related quantitative field.
• . 3-6 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.