The opportunity

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).

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

- LLM-powered platforms (RAG 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 - Associate Fraud and Federated AI

- Division: NPCI Market Innovation

- Experience: 1 to 3 Years

- Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field

- Employment Type: Full-time

- Location: Mumbai & 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

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

- Implement
- RAG pipelines

- 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

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

Valuable to have skills and experience required

- Experience in
- Payments / fintech / banking domain

- Fraud detection, AML, mule detection systems

- Exposure to

- Graph analytics on transactional data

- Federated learning & privacy-preserving AI

- Real-time streaming systems

- Experience with

- Cloud platforms (AWS/GCP/Azure)

- ML pipelines & MLOps frameworks

- Research experience

- Publications in ML/AI conferences or journals

- Ability to

- Design AI models inspired by mathematics/physics principles

📌 Associate Fraud and Federated AI (Mumbai)
🏢 NPCI
📍 Mumbai