Data Scientist

Job Description

Job Summary Title – GenAI Data Scientist Position Overview: We are seeking a talented AI/ML (Artificial Intelligence and Machine Learning) Data Scientist for project delivery. As a member of the team, you will play a crucial role in developing and implementing cutting-edge AI/ML solutions to drive innovation and deliver business value for clients. You will collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to develop and deploy AIML models and applications. This is an exciting opportunity to work on challenging projects and contribute to the advancement of AI/ML technologies. JD: Good hands-on experience in AI/ML libraries and frameworks (e.g., PyTorch, scikit-learn) along with proficiency in Python. Strong understanding of AI/ML concepts (machine learning and deep learning), and techniques with practical implementation experience. Understanding of model suitability for different problems. Experience in applying Gen AI based approaches along with NLP, both using LLM / SLMs and fine-tuning. Experience with data curation for SLM training will be a plus. Experience with end-to-end model creation lifecycle activities, including data preprocessing, feature engineering, model evaluation, deployment and data visualization. Design and execute statistical analyses to identify patterns and trends in data Excellent knowledge of statistical theory and methods Should have worked with APIs like Flask, Django, FastAPI, etc. Strong analytical and problem-solving skills, with the ability to analyze complex datasets and extract actionable insights. Excellent communication and collaboration skills to work effectively in cross-functional teams. Strong organizational and time management skills, with the ability to prioritize and manage multiple projects simultaneously. Experience of 12+ years with 8+ years of relevant experience. Must Have- 5 years of relevant hands-on experience Experience on both traditional ML and Deep Learning Worked on NLP use cases (beyond standard entity extraction, sentiment analysis, chatbots) Worked with Language Models in areas like advanced RAG, finetuning, optimization, agentic approaches (simple LLM integrations in chatbot or basic RAG will not be sufficient) Show more Show less