Senior Associate Data Science

In this role, you will work closely with data engineers, platform teams, and business stakeholders to design, experiment, and deploy machine learning and LLM‑powered solutions in production environments. You will contribute to innovation use cases such as fraud detection, intelligent complaint analysis (UPI Help), conversational AI, and insight generation from structured and unstructured datasets, while ensuring robustness, scalability, and regulatory alignment.

• Job Title: Data Scientist or AI Engineer
  
• Division / Department: Market Innovation
  
• Years of Experience: 3–6 years
  
• Education: BE/B.Tech / ME/M.Tech / MCA / MSc or equivalent (AI, ML, Data Science, Computer Science or related fields preferred)
  
• Employment Type: Full‑time
  
• Location: Mumbai
  
• Role Type: Permanent
  

• Develop and deploy machine learning and deep learning models on large‑scale financial and payment datasets.
  
• Build predictive and classification models for use cases such as fraud detection, anomaly identification, and transaction intelligence.
  
• Design and implement Generative AI and LLM‑powered applications for NLP‑driven use cases including complaint analysis, document understanding, and conversational AI.
  
• Experiment with large language models, prompt engineering techniques, and Retrieval Augmented Generation (RAG) frameworks to build reliable, grounded AI solutions.
  
• Design and implement Agent and Model Context Protocols (MCPs).
  
• Extract insights from structured, semi‑structured, and unstructured data using statistical analysis, NLP, and representation learning techniques.
  
• Work closely with data engineers and platform teams to integrate models into scalable data pipelines and production systems.
  
• Collaborate with product, operations, and business stakeholders to translate problem statements into data‑driven solutions and measurable outcomes.
  
• Evaluate model performance, conduct experiments, and continuously improve model accuracy, robustness, and efficiency.
  
• Ensure adherence to data security, compliance, and governance standards applicable to financial and payment systems.
  

• Strong proficiency in Python and commonly used AI/ML libraries (e.g., NumPy, Pandas, scikit‑learn).
  
• Experience working with Agents & MCPs.
  
• Hands‑on experience in machine learning and deep learning, including supervised and unsupervised learning techniques.
  
• Practical exposure to NLP techniques such as text classification, information extraction, embeddings, and language modeling.
  
• Experience working with Large Language Models (LLMs) and building applications using prompt engineering, fine‑tuning, or API‑based models.
  
• Solid understanding of Retrieval Augmented Generation (RAG) concepts, vector embeddings, and semantic search.
  
• Experience handling large‑scale datasets, feature engineering, and model evaluation.
  
• Familiarity with model deployment concepts and working with data pipelines in production environments.
  

• 3–6 years of hands‑on experience in data science, machine learning, or applied AI roles.
  
• Strong problem‑solving and analytical thinking abilities, with a structured approach to experimentation.
  
• Ability to translate business problems into ML/AI solutions and communicate results effectively to non‑technical stakeholders.
  
• Experience working in cross‑functional teams involving engineering, product, and business partners.
  
• Good understanding of the financial services or payments domain, including transaction data and risk‑related use cases.
  
• Strong verbal and written communication skills.
  

• Prior experience working in payments, banking, fintech, or regulated financial environments.
  
• Exposure to fraud detection, anomaly/Mule detection, or risk modeling systems.
  
• Experience with graph‑based models or network analytics on transactional data.
  
• Familiarity with cloud platforms and ML model deployment frameworks.
  
• Experience with ML Pipelines.
  

• Strong proficiency in Python and commonly used AI/ML libraries (e.g., NumPy, Pandas, scikit‑learn).
  
• Hands‑on experience in machine learning and deep learning, including supervised and unsupervised learning techniques.
  
• Practical exposure to NLP techniques such as text classification, information extraction, embeddings, and language modeling.
  
• Experience working with Large Language Models (LLMs) and building applications using prompt engineering, fine‑tuning, or API‑based models.
  
• Solid understanding of Retrieval Augmented Generation (RAG) concepts, vector embeddings, and semantic search.
  
• Experience handling large‑scale datasets, feature engineering, and model evaluation.
  
• Familiarity with model deployment concepts and working with data pipelines in production environments.