Senior Associate Data Science

Role Overview

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

• Division: NPCI Data Analytics – Market Innovation

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

• Experience Required: 3 to 6 Years

• 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

---

Good-to-Have Skills & Experience

• 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

• 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