Associate Data Science

Role: Associate Data Science

Industry Type: Financial Services

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Data Science & Machine Learning

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

UG: B.Tech / B.E. in Any Specialization

PG: MS/M.Sc(Science) in Any Specialization, M.Tech in Any Specialization, LLM in Law, MCA in Computers

Role Overview

You will be part of NPCI 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 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 :
  • PEMAIL_ADDRESS, 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

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