Job
Description
As a candidate for the role of spearheading the development and deployment of advanced analytics and data-driven solutions across the CBG value chain, your responsibilities will include: - Defining and owning the advanced analytics roadmap, aligning data initiatives with CBG business objectives such as yield maximization, cost reduction, and quality improvement. - Identifying high-impact use cases across feedstock selection, digester performance, purification, bottling, logistics, and sales forecasting. You will also be leading the design, development, and validation of statistical and machine-learning models including predictive maintenance, anomaly detection, yield forecasting, and optimization. Additionally, you will oversee end-to-end analytics workflows such as data ingestion/cleansing, feature engineering, model training, evaluation, and deployment. In this role, you will be required to evaluate, select, and manage analytics platforms and toolchains. You will drive the adoption of automated ML pipelines, MLOps best practices, and scalable data architectures. Collaboration with plant operations, supply chain, and commercial teams will be essential to translate analytical insights into actionable dashboards and reports using tools like Power BI, Tableau, or Grafana. Presenting findings and recommendations to senior leadership in clear business narratives will also be part of your responsibilities. You will mentor and lead a small team of data scientists, analysts, and engineers while fostering a culture of experimentation, continuous learning, and cross-functional collaboration. Establishing data governance standards, model validation protocols, and ensuring compliance with data security, privacy, and regulatory requirements will also fall under your purview. Qualifications required for this position include a B.E. / B.Tech degree and 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 is also a key skill set expected from you. As a candidate for the role of spearheading the development and deployment of advanced analytics and data-driven solutions across the CBG value chain, your responsibilities will include: - Defining and owning the advanced analytics roadmap, aligning data initiatives with CBG business objectives such as yield maximization, cost reduction, and quality improvement. - Identifying high-impact use cases across feedstock selection, digester performance, purification, bottling, logistics, and sales forecasting. You will also be leading the design, development, and validation of statistical and machine-learning models including predictive maintenance, anomaly detection, yield forecasting, and optimization. Additionally, you will oversee end-to-end analytics workflows such as data ingestion/cleansing, feature engineering, model training, evaluation, and deployment. In this role, you will be required to evaluate, select, and manage analytics platforms and toolchains. You will drive the adoption of automated ML pipelines, MLOps best practices, and scalable data architectures. Collaboration with plant operations, supply chain, and commercial teams will be essential to translate analytical insights into actionable dashboards and reports using tools like Power BI, Tableau, or Grafana. Presenting findings and recommendations to senior leadership in clear business narratives will also be part of your responsibilities. You will mentor and lead a small team of data scientists, analysts, and engineers while fostering a culture of experimentation, continuous learning, and cross-functional collaboration. Establishing data governance standards, model validation protocols, and ensuring compliance with data security, privacy, and regulatory requirements will also fall under your purview. Qualifications required for this position include a B.E. / B.Tech degree and 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 is also a key skill set expected from you.
