Assistant Manager

About Company

Founded in 2011, ReNew, is one of the largest renewable energy companies globally, with a leadership position in India. Listed on Nasdaq under the ticker RNW, ReNew develops, builds, owns, and operates utility-scale wind energy projects, utility-scale solar energy projects, utility-scale firm power projects, and distributed solar energy projects. In addition to being a major independent power producer in India, ReNew is evolving to become an end-to-end decarbonization partner providing solutions in a just and inclusive manner in the areas of clean energy, green hydrogen, value-added energy offerings through digitalisation, storage, and carbon markets that increasingly are integral to addressing climate change. With a total capacity of more than 13.4 GW (including projects in pipeline), ReNew's solar and wind energy projects are spread across 150+ sites, with a presence spanning 18 states in India, contributing to 1.9 % of India's power capacity. Consequently, this has helped to avoid 0.5% of India's total carbon emissions and 1.1% India's total power sector emissions. In the over 10 years of its operation, ReNew has generated almost 1.3 lakh jobs, directly and indirectly. ReNew has achieved market leadership in the Indian renewable energy industry against the backdrop of the Government of India's policies to promote growth of this sector. ReNew stands committed to providing clean, safe, affordable, and sustainable energy for all and has been at the forefront of leading climate action in India.

Job Description

As a Data Scientist, you will play a key role in designing, building, and deploying scalable machine learning solutions, with a focus on real-world applications including Generative AI, optimization, forecasting, and operational analytics. You will work closely with data scientists, engineers, and business stakeholders to take AI models from ideation to production, ensuring high-quality delivery and integration within ReNew's technology ecosystem.

Roles and Responsibilities

• Build and deploy production-grade ML pipelines for varied use cases across operations, manufacturing, supply chain, and more
• Work hands-on in designing, training, and fine-tuning models across traditional ML, deep learning, and GenAI (LLMs, diffusion models, etc.)
• Collaborate with data scientists to transform exploratory notebooks into scalable, maintainable, and monitored deployments
• Implement CI/CD pipelines, version control, and experiment tracking using tools like MLflow, DVC, or similar
• Do shadow deployment and A/B testing of production models
• Partner with data engineers to build data pipelines that support real-time or batch model inference
• Ensure high availability, performance, and observability of deployed ML solutions using MLOps best practices
• Conduct code reviews, performance tuning, and contribute to ML infrastructure improvements
• Support the end-to-end lifecycle of ML products
• Contribute to knowledge sharing, reusable component development, and internal upskilling initiatives

Eligibility Criteria

• Bachelor's in Computer Science, Engineering, Data Science, or related field. Master's degree preferred
• 4–6 years of experience in developing and deploying machine learning models, with significant exposure to MLOps practices
• Experience in implementing and productionizing Generative AI applications using LLMs (e.g., OpenAI, HuggingFace, LangChain, RAG architectures)
• Strong programming skills in Python; familiarity with ML libraries such as scikit-learn, TensorFlow, PyTorch
• Hands-on experience with tools like MLflow, Docker, Kubernetes, FastAPI/Flask, Airflow, Git, and cloud platforms (Azure/AWS)
• Solid understanding of software engineering fundamentals and DevOps/MLOps workflows
• Exposure to at least 2-3 industry domains (energy, manufacturing, finance, etc.) preferred
• Excellent problem-solving skills, ownership mindset, and ability to work in agile cross-functional teams

Main Interfaces

The role will involve close collaboration with data scientists, data engineers, business stakeholders, platform teams, and solution architects.