Assistant Manager - Data Scientist

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

Qualifications

• 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