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.