Data Scientist (VP/AVP)

Key Responsibilities • Design, develop, and deploy machine learning and deep learning models for business applications. • Build AI-driven solutions covering recommendation systems, Next Best Action, predictive analytics, portfolio optimisation, and risk modelling. • Develop Generative AI applications using LLMs, RAG, Prompt Engineering, and Agentic AI frameworks. • Build and maintain scalable AI/ML pipelines and production-ready solutions using MLOps/LLMOps practices. • Collaborate with business stakeholders, product owners, and engineering teams to industrialize AI solutions. • Contribute to AI Centre of Excellence development, AI governance, and responsible AI adoption. • Implement model monitoring, explainability, performance optimization, and data quality practices. Key Requirements • Master's or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related field. • 3–7+ years of hands-on experience in Data Science, AI, or Machine Learning. • Strong programming skills in Python and SQL. • Experience with machine learning, Deep Learning, NLP, and frameworks such as PyTorch, TensorFlow, and Scikit-learn. • Hands-on experience with Generative AI, LLMs, RAG, Prompt Engineering, and Agentic AI. • Familiarity with LangChain, LangGraph, vector databases, and embedding models. • Experience with cloud platforms (AWS/Azure/GCP), Docker, Kubernetes, CI/CD, and MLOps/LLMOps. • Experience developing and deploying AI/ML models in production environments. Preferred Experience • Banking, Financial Services, or Wealth Management domain experience. • Exposure to investment analytics, portfolio optimisation, recommendation systems, or personalisation. • Experience in AI governance or setting up an AI Centre of Excellence. • Strong stakeholder management and communication skills.