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.

Skills

TensorFlow, Ai, Generative AI Application Development and Deployment, AI Governance, Model Deployment, Pipeline Development, Azure Machine Learning, Computer Science, LangChain, Cloud Infrastructure, CI/CD, Generative AI Prompt Engineering, Recommender Systems, Performance Tuning, Collaboration With Stakeholders, Programming