Job Purpose:

To spearhead the development and deployment of advanced analytics and data-driven solutions across the CBG value chain—driving process optimization, predictive insights, and innovation in production, maintenance, supply chain, and commercial operations.

Key Responsibilities:

Analytics Strategy & Roadmap:

• Define and own the advanced analytics roadmap, aligning data initiatives with CBG business objectives (yield maximization, cost reduction, quality improvement).
• Identify high-impact use cases across feedstock selection, digester performance, purification, bottling, logistics, and sales forecasting.

Data Modelling & Solution Development:

• Lead the design, development, and validation of statistical and machine-learning models (predictive maintenance, anomaly detection, yield forecasting, optimization).
• Oversee end-to-end analytics workflows: data ingestion/cleansing, feature engineering, model training, evaluation, and deployment.

Platform & Tool Leadership:

• Evaluate, select, and manage analytics platforms and toolchains
• Drive the adoption of automated ML pipelines, MLOps best practices, and scalable data architectures.

Insight Delivery & Visualization:

• Collaborate with plant operations, supply chain, and commercial teams to translate analytical insights into actionable dashboards and reports (Power BI/Tableau/Grafana).
• Present findings and recommendations to senior leadership, translating complex analyses into clear business narratives.

Team Leadership & Collaboration:

• Mentor and lead a small team of data scientists, analysts, and engineers; coordinate with IT, digital infrastructure, and manufacturing applications leads.
• Foster a culture of experimentation, continuous learning, and cross-functional collaboration.

Governance & Compliance:

• Establish data governance standards, model validation protocols, and documentation for reproducibility and audit readiness.
• Ensure compliance with data security, privacy, and regulatory requirements.

Key Skills:

• B.E. / B.Tech  
• Strong proficiency in Python, SQL, ML frameworks (scikit-learn, TensorFlow, PyTorch), and big-data technologies (Spark, Databricks, etc.).
• Hands-on experience with predictive maintenance, process optimization, time-series forecasting, and anomaly detection.