Senior Manager

Role Overview

Manager – Data Science & Generative AI

Key Responsibilities

• Lead the end-to-end lifecycle of data science and Generative AI projects—from problem scoping and data exploration to model deployment and performance monitoring.
• Manage a team of data scientists and ML engineers, providing technical guidance and mentorship.
• Develop and implement advanced machine learning models including deep learning, NLP, and GenAI architectures (e.g., transformers, LLMs).
• Collaborate with product, engineering, and business teams to identify AI opportunities, define use cases, and deploy innovative solutions.
• Evaluate and fine-tune pre-trained foundation models (e.g., GPT, Claude, LLaMA, Gemini) for domain-specific applications using prompt engineering, fine-tuning, or retrieval-augmented generation (RAG).
• Lead the creation of synthetic data, content generation, and intelligent assistants using GenAI capabilities.
• Drive responsible AI practices and ensure model transparency, fairness, and compliance with ethical and regulatory guidelines.
• Contribute to strategic roadmaps for AI adoption across the enterprise.
• Present findings, models, and strategies to stakeholders and senior leadership.
  

Required Skills & Experience:

• 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 (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch).
• Proven experience in building and deploying ML models in production environments.
• Solid experience working with Generative AI tools and platforms (e.g., OpenAI APIs, Hugging Face, LangChain, Vertex AI, Azure OpenAI).
• Expertise in NLP, LLMs, prompt engineering, fine-tuning, and text generation.
• Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps pipelines.
• Strong understanding of data engineering concepts, APIs, and scalable architectures.
• Excellent problem-solving, project management, and communication skills.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field (PhD is a plus).
  

Nice to Have:

• Experience with Retrieval-Augmented Generation (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.