As a **Senior Data Scientist (Manufacturing \& Process AI)**, you will build and deploy machine learning models directly on plant data to significantly cut energy consumption, improve critical equipment reliability, and tighten product quality across our operations.
This is a highly collaborative, hands\-on modeling role embedded within our process, operations, and reliability teams. Your success will be measured by finance\-validated operational savings (e.g., kcal/kg clinker, kWh/tonne, and avoided downtime).
- **ML Modeling for Industry:** Build, validate, and deploy ML models for process optimization (kiln/pyro\-process control, grinding \& separator efficiency), predictive maintenance on critical rotating equipment, and quality/clinker\-factor optimization.
- **Industrial Data Wrangling:** Work with high\-frequency sensor and time\-series data from plant historians, DCS, and IIoT systems. Clean and engineer meaningful features from noisy, real\-world industrial signals.
- **Bridge the Gap (OT to AI):** Partner closely with plant operators and process engineers to encode domain knowledge into your models. Safely guide models from advisory recommendations toward closed\-loop control.
- **Business \& Financial Rigor:** Establish rigorous baselines and quantify financial impact with institutional discipline, defending data results under scrutiny.
- **Production Deployment:** Collaborate with the MLOps/Platform team to productionize models and monitor their drift and performance in live operations.
- *Required Qualifications \& Skills (Must\-Have)Education \& Experience**
- **Degree:** Bachelor's or Master’s degree in Engineering (Chemical, Mechanical, Electrical, Industrial), Statistics, Computer Science, or a related quantitative field.
- **Experience:** 3–6 years of experience building and deploying ML models, with **demonstrable experience in a manufacturing or process\-industry environment** (e.g., cement, steel, refining, chemicals, power, glass, mining).
- **Core Machine Learning:** Deep command of classical machine learning including regularized regression (Ridge, Lasso, ElasticNet), tree\-based ensembles (Random Forest, XGBoost, LightGBM, CatBoost), SVM, k\-NN, and Naive Bayes.
- **Data Science \& Engineering:** Strong, idiomatic Python (NumPy, pandas, SciPy, scikit\-learn, statsmodels) writing clean, tested, production\-quality code. Strong SQL skills are mandatory.
- **Time\-Series \& Analytics:** Strong applied skills in time\-series analysis, sensor/signal data processing, anomaly detection, regression, and forecasting with a solid statistics foundation.
- **Soft Skills:** Comfortable being on the plant floor, explaining complex models simply to engineers and operators to earn their trust.
- *Preferred Qualifications (Strong Plus)**
- **OT Data Experience:** Hands\-on experience with Industrial IoT (IIoT) and Operational Technology (OT) data—plant historians (OSIsoft PI / AVEVA, Aspen IP.21\), OPC\-UA, SCADA / DCS, and time\-series databases.
- **Domain Exposure:** Background in cement or heavy/process manufacturing (pyroprocessing, grinding, combustion, quality control).
- **Enterprise Systems:** Experience working with data from SAP (ERP—specifically PM/PP modules) and Salesforce (SFDC).
- **Advanced Controls:** Familiarity with Advanced Process Control (APC) concepts and closed\-loop deployment.
- **Advanced Modeling:** Deep learning for time series; physics\-informed or hybrid (data \+ first\-principles) modeling.
- **Big Data:** Experience with PySpark for large datasets.
- *Technical Environment \& Tools**
- **Programming:** Python (pytest, OOP), SQL, Jupyter, Git
- **Libraries:** Scikit\-learn, Statsmodels, XGBoost, LightGBM, Matplotlib, Seaborn, sktime, tsfresh, Prophet
- **Unsupervised:** K\-means, DBSCAN, Hierarchical Clustering, PCA
- **Platforms:** Cloud/Lakehouse (Azure, AWS, or Databricks), Git\-based workflows.
Pay: Up to ₹1,200,000\.00 per year
Application Question(s)
* Notice Period / Joining Timeline
This is an urgent requirement. Are you available to join within immediate to 1 week maximum?
Options: Yes / No
- This position is being filled as a Vendor Contract role. Are you comfortable working under a vendor contract engagement?
Options: Yes / No
- Do you have demonstrable experience building and deploying ML models specifically within a manufacturing or heavy process\-industry environment (e.g., cement, steel, refining, chemicals, power, glass, mining)?
- current salary per month?
- expected salary per month?
Experience
- Senior Data Scientist (Manufacturing \& Process AI): 3 years (Required)
Work Location: In person