Contractor - Data Science & AI

Position: Contractor - Data Science & AI Job
Job Description :
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 product ionise 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.
. 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 (Num Py, pandas, Sci Py, scikit-learn,
stats models) with clean, tested, production-quality code strong SQL.
. Deep command of classical / traditional machine learning - regularised regression
(Ridge, Lasso, Elastic Net), tree-based ensembles (Random Forest, Gradient Boosting
- XGBoost / LightGBM / Cat Boost), 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 - Num Py, pandas and
Sci Py 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 / Cat Boost), 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 (stats models, sktime, tsfresh, Prophet)
anomaly detection (Isolation Forest, One-Class SVM).
. Core libraries: scikit-learn, stats models, 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