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.