Associate Data Science

You will be part of NPCI's Market Innovation team, working at the intersection of advanced machine learning, deep learning, graph AI, and Generative AI to build next-generation intelligent systems for India's digital payments ecosystem.

This role focuses on solving India-scale problems such as fraud detection, mule/AML risk modeling, transaction intelligence, and conversational AI, using both classical ML and cutting-edge AI architectures (LLMs, GNNs, Transformers, Agentic AI systems).

You will design end-to-end AI systems—from problem formulation, feature engineering, and model development to GPU-accelerated optimization and production deployment, ensuring low latency, scalability, and robustness.

The role offers a unique opportunity to work on:

• Graph-based fraud detection systems
• Agentic AI & LLM-powered platforms (RAG, MCP, workflows)
• GPU/CUDA optimized AI pipelines
• Privacy-preserving and federated AI systems

You will collaborate with top academic institutions (IITs/IISc) and cross-functional teams to push the boundaries of applied AI in financial systems.

Job Details

• Job Title: Data Scientist – AI Engineer
• Division: NPCI Data Analytics – Market Innovation
• Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field
• Employment Type: Full-time
• Location: Hyderabad
• Role Type: Permanent

Key Responsibilities

Machine Learning & Advanced Modeling

• Develop and deploy ML/DL models (Logistic Regression, RF, XGBoost, NN, CNN, Transformers, GANs)
• Build models for fraud detection, AML, anomaly detection, transaction intelligence
• Work on imbalanced datasets using advanced sampling and cost-sensitive learning

Graph AI & Advanced Systems

• Design Graph AI models: GNN, GCN, GAT, temporal graph networks
• Apply network analytics for fraud rings, mule detection, behavioral risk signals

Generative AI & Agentic Systems

• Build LLM-powered applications (chatbots, complaint intelligence, document analysis)

• Implement:

  • RAG pipelines
  • Agentic workflows & MCP (Model Context Protocols)
  • Prompt engineering & LLM fine-tuning

Feature Engineering & Data Science

• Perform EDA, feature engineering (temporal, behavioral, aggregated features)
• Work with structured, semi-structured, and unstructured data

Model Optimization & GPU Acceleration

• Optimize models for:

  • Latency & throughput
  • GPU performance (CUDA-based optimization)

• Use libraries such as:

  • RAPIDS, cuDF, cuML, cuGraph, PyTorch Geometric

Evaluation & Experimentation

• Design custom loss functions (weighted BCE, cost-sensitive)

• Apply business-aligned metrics:

  • Precision@K, Recall, ROC-AUC, PR-AUC

• Use robust validation techniques (cross-validation, time-based splits)

Deployment & Production Systems

• Integrate models into batch and real-time production systems

• Design scalable ML pipelines & APIs

• Monitor:

  • Model drift
  • Performance stability
  • Business impact

Collaboration & Research

• Work with data engineers, product teams, and business stakeholders
• Contribute to research, innovation, and academic collaborations
• Stay updated on latest AI advancements (LLMs, Graph AI, Federated Learning)

Requirements

Required Technical Skills

Core ML & Data Science

• Strong in:

  • Supervised & unsupervised learning
  • Statistical modeling (Logistic Regression, DA)
  • Tree models (RF, XGBoost, LightGBM)

• Deep Learning:

  • NN, CNN, Transformers, GANs

Generative AI & LLM Stack

• Hands-on experience with:

  • LLMs (OpenAI, open-source models)
  • Prompt engineering, fine-tuning
  • RAG pipelines & vector databases
  • Agent frameworks & MCPs

Graph AI

• Experience with:

  • GNN, GCN, GAT
  • Graph-based fraud detection
  • Network analytics

Programming & Tools

• Strong proficiency in:

  • Python (NumPy, Pandas, scikit-learn)
  • SQL (large-scale data processing)

• Frameworks:

  • PyTorch / TensorFlow
  • PyTorch Geometric

---

Key Skills and Experience Required

• Strong foundation in:

  • Mathematics, probability, statistics
  • Data structures & algorithms

• Expertise in:

  • Feature engineering & model evaluation
  • Handling large-scale datasets

• Experience with:

  • Imbalanced datasets & sampling techniques
  • Custom loss functions & business metrics

• Knowledge of:

  • Model deployment & production pipelines
  • Model monitoring & performance tracking

• Strong:

  • Problem-solving ability
  • Communication & stakeholder management

• Ability to translate business problems into scalable AI systems