Job
Description
We are seeking an experienced and hands-on Senior Data Scientist with expertise in Generative AI and/or Classical Machine Learning to join our growing AI team. In this role, you will lead the design and development of data-driven solutions , ranging from traditional ML models to cutting-edge GenAI applications that solve real business problems. Key Responsibilities: Develop, validate, and deploy ML models for prediction, classification, recommendation, and other business use cases. Design and build GenAI applications using LLMs (e.g., GPT, LLaMA, Claude) for use cases like summarization, Q&A, content generation, document analysis, etc. Collaborate with stakeholders to translate business problems into data science solutions . Perform EDA, feature engineering , and data preprocessing on structured and unstructured data (text, images, documents). Implement model performance monitoring , drift detection, and retraining strategies. Work with MLOps/engineering teams to ensure scalable, production-grade model deployment. Stay up to date with new GenAI and ML research trends and identify opportunities for business impact. Mentor junior data scientists and support peer review of code, model design, and documentation. Required Qualifications: Education: Masters or Bachelor's degree in Computer Science, Data Science, Statistics, or related field. Experience: 58 years of professional experience in data science , with proven delivery of ML/AI models in production. Technical Skills: Strong programming skills in Python and use of libraries such as Pandas, Scikit-learn, NumPy, TensorFlow, PyTorch. Experience building and fine-tuning ML models for regression, classification, clustering, or time-series forecasting. Hands-on experience with LLMs and GenAI frameworks (e.g., LangChain, Hugging Face Transformers, OpenAI API, RAG pipelines). Exposure to text embeddings, prompt engineering, vector databases (e.g., FAISS, Pinecone, Chroma). Proficient in using SQL and working with large datasets. Familiarity with model deployment tools and practices (Docker, FastAPI, MLflow, Streamlit, or similar). Experience with cloud platforms (AWS, Azure, or GCP) is a strong plus. Preferred Skills: Experience with NLP, Computer Vision , or time-series modeling . Exposure to MLOps workflows (CI/CD, monitoring, pipeline automation). Familiarity with vector search, RAG architecture , and document intelligence workflows. Previous experience in domains like BFSI, healthcare, retail, or enterprise automation is a plus. Key Attributes: Strong business acumen and stakeholder communication. Ability to balance research mindset with production-readiness . Passion for innovation, continuous learning, and cross-functional collaboration. Team player with mentoring and leadership qualities.
