Data Scientist I

Responsibilities

• Build, train, and evaluate machine learning models across traditional ML, NLP, and LLM/GenAI use cases, under the guidance of senior team members
• Contribute to data pipelines and feature engineering workflows in Databricks using PySpark and Delta Lake
• Support model deployment and monitoring on Azure - including Azure AI Foundry, Azure OpenAI, and Azure Functions - and help maintain production model health
• Contribute to LLM and generative AI workflows - including prompt engineering, RAG pipelines, and agentic applications built on frameworks such as LangChain or LlamaIndex - under the guidance of senior team members
• Conduct exploratory data analysis and communicate findings clearly through code, documentation, and team presentations
• Write clean, well-tested, and reproducible Python code; contribute to shared codebases and track experiments via MLflow
• Partner with senior data scientists and cross-functional stakeholders to understand business problems and translate them into analytical approaches
• Participate actively in code reviews, team rituals, and knowledge-sharing sessions
• Develop your skills proactively - engage with new tools, research, and techniques relevant to the team's work

Requirements

• Bachelor's or Master's degree in computer science, statistics, mathematics, data science, or a related quantitative field
• 1-3 years of practical data science or ML experience (internships, co-ops, research, and strong project work all count)
• Proficiency in Python and familiarity with core ML libraries (scikit-learn, pandas, NumPy)
• Solid understanding of foundational ML concepts: supervised and unsupervised learning, model evaluation, cross-validation, and feature engineering
• Exposure to at least one deep learning or NLP framework (PyTorch, TensorFlow, or Hugging Face Transformers) and familiarity with LLM concepts such as prompt engineering, embeddings, or retrieval-augmented generation
• Comfort working with structured and unstructured data, including text and document-based sources
• Basic understanding of the ML lifecycle - from data preparation and experimentation through evaluation and handoff
• Clear, organised communication skills - able to document work and explain methods to peers and stakeholders
• Curiosity, rigour, and a strong drive to learn in a fast-paced, collaborative environment

Preferred

• Hands-on experience with Databricks, Spark/PySpark, or cloud ML platforms (Azure AI Foundry, AWS SageMaker, or GCP Vertex AI)
• Hands-on experience with LLM/GenAI and agentic workflows - prompt engineering, RAG, embeddings, vector databases, or building with frameworks such as LangChain, LlamaIndex, or Semantic Kernel
• Familiarity with MLflow or other experiment tracking and model versioning tools
• Experience in financial services, risk, compliance, or a regulated industry
• Knowledge of responsible AI principles, including fairness, transparency, and data privacy

Location - New Delhi,Bengaluru,Hyderabad,Mumbai