Data Engineering & Architecture

- Lead the design and development of scalable ETL/ELT pipelines for batch and real-time data ingestion from manufacturing, energy, and operations systems.
- Architect data platforms supporting plant operations, energy consumption, emissions tracking, quality, and supply chain data.
- Work with big data technologies such as Hadoop, Spark, Kafka, and Snowflake for large-scale data processing.
- Establish and enforce data governance, quality, security, and compliance standards across enterprise data assets.

Level: M ( L4)

Senior Data Scientist

Role Overview

We are seeking a Senior Data Scientist who can lead end-to-end data and AI initiatives across data engineering, advanced analytics, and machine learning. The ideal candidate will bring strong domain expertise in Metals, Clean Energy, and Paints & Coatings industries, applying data science to drive operational excellence, sustainability, quality, and profitability.

This role requires deep technical expertise, domain understanding, and the ability to design, deploy, and scale production-grade data and AI solutions.

Key Responsibilities

Data Engineering & Architecture

- Lead the design and development of scalable ETL/ELT pipelines for batch and real-time data ingestion from manufacturing, energy, and operations systems.
- Architect data platforms supporting plant operations, energy consumption, emissions tracking, quality, and supply chain data.
- Work with big data technologies such as Hadoop, Spark, Kafka, and Snowflake for large-scale data processing.
- Establish and enforce data governance, quality, security, and compliance standards across enterprise data assets.

Data Analysis & Visualization

- Perform advanced exploratory data analysis (EDA) to uncover trends, anomalies, and improvement opportunities in complex industrial datasets.




- Develop domain-driven analytics for:
- Metals: process optimization, yield improvement, quality prediction, energy efficiency, and predictive maintenance
- Clean / New Energy: asset performance analysis, production forecasting, energy optimization, and sustainability metrics
- Paints & Coatings: formulation optimization, batch consistency, defect analysis, and demand forecasting

- Design intuitive dashboards and reports using Tableau, Power BI, or Looker for business and operational stakeholders.
- Optimize and review complex SQL queries and analytical data models to improve performance and scalability.

Machine Learning, AI & MLOps

- Lead the development and deployment of machine learning models for use cases such as predictive maintenance, process optimization, anomaly detection, demand forecasting, and quality analytics.
- Apply ML frameworks including TensorFlow, PyTorch, and Scikit-learn, with a strong focus on time-series and industrial data.
- Implement and mature MLOps practices, including model versioning, monitoring, CI/CD pipelines, and A/B testing.
- Deploy models through APIs and cloud-native services, ensuring reliability, scalability, and explainability.
- Support sustainability and resource-efficiency initiatives through data-driven insights and AI solutions.

Leadership & Collaboration

- Mentor and guide junior and mid-level data scientists through technical leadership, code reviews, and best practices.




- Act as a trusted data science advisor to engineering, operations, R&D;, and business leadership.
- Collaborate closely with process engineers, energy specialists, and manufacturing teams to translate domain challenges into analytical and ML solutions.
- Contribute to data science standards, reusable frameworks, and long-term AI strategy at the enterprise level.

Required Skills & Qualifications

- Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, or a related discipline.
- 8+ years of hands-on experience in data science, advanced analytics, or machine learning roles.
- Strong programming skills in Python and SQL; working knowledge of R, Scala, or Java is a plus.
- Extensive experience with Big Data frameworks such as Hadoop, Spark, Kafka, and orchestration tools like Airflow.
- Robust foundation in machine learning, deep learning, statistical modeling, and time-series analysis.
- Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).

Preferred Qualifications (Domain-Focused)

- Proven experience in one or more of the following industries:
- Metals and Mining (Copper, steel, aluminum, specialty metals, smelting, rolling, finishing operations)
- Clean / New Energy or Industrial Energy Systems (energy optimization, asset performance, emissions analytics)
- Paints, Coatings, or Chemicals (formulation, batch processes, quality control, R&D; analytics)

- Experience applying data science to manufacturing, process engineering, supply chain, or sustainability use cases.
- Exposure to industrial IoT, sensor data, SCADA/MES systems, and real-time analytics.
- Experience with MLOps tools such as MLflow, Kubeflow, and Airflow.

📌 Senior Data Scientist- CWR (Hyderabad)
🏢 Arminus
📍 Hyderabad