Data Scientist

Description - External

Ready to build the future with AI?
At Genpact, we dont just keep up with technology—we set the pace. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies’ most complex challenges. If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at genpact.com and on LinkedIn, X, YouTube, and Facebook.

Inviting applications for the role Lead Consultant – Data Scientist – Generative AI of & Advanced Analytics Job Summary: We are looking for a highly motivated Data Scientist with experience in Generative AI (GenAI) to join our advanced analytics team. In this role, you will blend classical data science with cutting-edge GenAI techniques (LLMs, embeddings, RAG) to solve real-world business problems, build intelligent systems, and push the boundaries of AI adoption across domains.

Key Responsibilities
• Build and deploy Generative AI models using large language models (LLMs) like GPT,Gemini ,Claude, LLaMA, etc. • Design and develop Retrieval-Augmented Generation (RAG) pipelines for enterprise search and summarization. • Perform exploratory data analysis, feature engineering, and apply statistical methods to derive insights. • Work with vector databases (e.g., FAISS, Pinecone, Chroma) and embedding models for semantic search. • Develop and fine-tune prompts for prompt engineering, few-shot learning, and function calling. • Collaborate with product and engineering teams to design AI-driven features and solutions. • Conduct A/B testing, build dashboards, and contribute to ML pipelines in production. • Stay updated with GenAI research and open-source tools; evaluate new models and frameworks. • solving high-impact problems involving structured and unstructured data using a wide range of ML/DL techniques. • The ideal candidate is hands-on, self-driven, and passionate about turning data into intelligent systems. • Strong Capabilities on SQL, Python, Vscode/pytorch and have come across using Tensorflow, Keras

Qualifications we seek in you!
Minimum Qualifications Required: • Bachelor's or master’s degree in data science, Computer Science, Statistics, or a related field. • experience in data science, machine learning, or applied AI roles. • Hands-on experience with GenAI platforms (OpenAI, Anthropic, Hugging Face, Cohere, etc.). • Strong Python skills: pandas, scikit-learn, transformers, LangChain, etc. • Experience working with LLMs, embeddings, or chatbot development. • Familiarity with MLOps, model versioning, and deployment in cloud environments (AWS/GCP/Azure). • Experience with RAG architecture, vector databases, and knowledge graphs. • Prior work in text summarization, document intelligence, or multi-modal AI. • Understanding of LLM fine-tuning, PEFT/LoRA, and instruction-tuned models. • Publications, open-source contributions, or GenAI product demos are a strong plus. • Strong in Python, with experience in NumPy, pandas, scikit-learn. • Proficiency in TensorFlow, PyTorch, or JAX. • Familiarity with deep learning concepts: backpropagation, activation functions, loss optimization. • Experience with cloud platforms: AWS, GCP, or Azure. • Comfortable with version control, CI/CD pipelines, and containerized environments (e.g., Docker).

Tools & Stack You’ll Work With
• LLMs: GPT-4, Gemini • Libraries: Hugging Face Transformers, LangChain, OpenAI API • Vector Stores: FAISS, Pinecone, Weaviate • ML Libraries: Scikit-learn, MLflow, DVC, PyTorch, TensorFlow, XGBoost, scikit-learn • Storage: PostgreSQL, S3, BigQuery • Deployment: Docker, FastAPI, Airflow, Kubernetes • Cloud: AWS/GCP/Azure

Role & responsibilities

Preferred candidate profile