About this role
Role Overview: We are seeking a highly skilled and forward-thinking Manager – Data Science & Generative AI to lead data-driven innovation. This role requires a blend of technical expertise in machine learning and GenAI, team leadership, and strategic thinking to build scalable solutions and drive business transformation through intelligent systems. You will lead cross-functional teams, guide AI/ML project lifecycles, and deploy Generative AI models into real-world applications. Key Responsibilities: Lead the end-to-end lifecycle of data science and Generative AI projects from problem scoping to deployment and monitoring. Manage a team of data scientists and ML engineers, providing technical guidance and mentorship. Develop advanced machine learning models including deep learning, NLP, and GenAI architectures. Collaborate with product and engineering teams to identify AI opportunities and define use cases. Evaluate and fine-tune foundation models (e.g., GPT, Claude, LLaMA, Gemini) using prompt engineering, fine-tuning, or RAG. Lead the creation of synthetic data and intelligent assistants. Drive responsible AI practices and ensure model transparency and compliance. Contribute to strategic AI roadmaps and present findings to senior leadership. Required Qualifications: 6–10 years of experience in Data Science or AI/ML, including at least 2–3 years in a managerial or lead role. Strong proficiency in Python and data science libraries. Proven experience in building and deploying ML models in production. Expertise in NLP, LLMs, prompt engineering, and text generation. Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps pipelines. Strong understanding of data engineering and scalable architectures. Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field (PhD is a plus). Preferred Qualifications: Experience with RAG and vector databases (e.g., FAISS, Pinecone, Weaviate). Familiarity with model safety, explainability (XAI), and ethical AI frameworks. Publications or contributions in AI/ML conferences or open-source projects. Hands-on experience in GenAI use cases like document summarization, chatbots, code generation, or marketing automation.