Lead Data Scientist

The Opportunity

You will be part of NPCI's Market Innovation & AI Engineering team, driving the development of next-generation Machine Learning and Deep Learning systems powering India's largest digital payments ecosystem.

This role focuses on solving large-scale real-world problems using advanced Machine Learning, Statistical Modeling, Deep Learning, Graph Analytics, and Distributed AI Systems. You will be responsible for designing, building, and scaling production-grade AI platforms that process billions of transactions while maintaining high standards of latency, reliability, explainability, and operational excellence.

The role offers a unique opportunity to work on:

• Fraud Detection & Risk Modeling

• AML & Financial Crime Analytics

• Behavioral Intelligence Systems

• Transaction Intelligence Platforms

• Large Scale Anomaly Detection Systems

• Graph-based Network Analytics

• Real-Time Decisioning Systems

• GPU Accelerated AI Workloads

You will collaborate closely with engineering teams, data platform teams, domain experts, and leading academic institutions to build cutting-edge AI solutions for large-scale financial ecosystems.

Job Details

Job Title Lead Data Scientist

Division Market Innovation

Experience 6-10 Years

Education B.Tech / M.Tech / MSc / MCA in Computer Science, AI, Data Science, Statistics, Mathematics or related fields

Employment Type Full Time

Location Hyderabad, Mumbai

Role Type Permanent

Key Responsibilities

Machine Learning & Advanced Modeling

• Design, develop, and deploy advanced Machine Learning solutions using supervised and unsupervised learning techniques.

• Build and optimize predictive models for fraud detection, anomaly detection, transaction intelligence, risk scoring, and behavioral analytics.

• Develop robust feature engineering frameworks for large-scale transactional datasets.

• Design custom evaluation methodologies and business-aligned performance metrics.

• Build explainable and interpretable AI systems for regulated environments.

Deep Learning Systems

• Develop Deep Learning models utilizing Neural Networks, CNNs, RNNs, LSTMs, Autoencoders, Transformers, and Representation Learning techniques.

• Optimize model architectures for production deployment and inference efficiency.

• Design custom loss functions and optimization strategies for imbalanced datasets and complex business problems.

Production AI & MLOps

• Design and implement scalable ML deployment pipelines.

• Productionize Machine Learning models using containerized infrastructure.

• Develop automated CI/CD workflows for model deployment and version management.

• Implement model monitoring, drift detection, performance tracking, and rollback mechanisms.

AI Platform Engineering

• Build high-throughput, low-latency inference platforms capable of supporting large transaction volumes.

• Design feature engineering and feature serving architectures for real-time prediction use cases.

• Collaborate with platform teams to optimize compute, storage, and data access layers.

Distributed AI Systems

• Develop machine learning workflows for large-scale datasets utilizing distributed computing frameworks.

• Build scalable data processing pipelines capable of handling billions of events and transactions.

• Optimize training and inference workloads across multi-node environments.

Research & Innovation

• Evaluate emerging techniques in Machine Learning and Deep Learning.

• Contribute to innovative solutions, intellectual property creation, technical whitepapers, and research publications.

• Collaborate with academic partners and industry experts on advanced AI initiatives.

Requirements

Required Technical Skills

Core Machine Learning

• Strong understanding of:

• Supervised Learning

• Unsupervised Learning

• Ensemble Learning

• Statistical Learning

• Reinforcement Learning Fundamentals

• Classification & Regression Techniques

• Anomaly Detection

• Time Series Forecasting

Mathematics & Statistics

• Probability Theory

• Inferential Statistics

• Hypothesis Testing

• Bayesian Statistics

• Optimization Techniques

• Linear Algebra

• Calculus for Machine Learning

Deep Learning

• Artificial Neural Networks (ANN)

• CNN

• RNN

• LSTM

• GRU

• Autoencoders

• Representation Learning

• Transformers

• Model Optimization Techniques

Programming & Data Engineering

• Python

• NumPy

• Pandas

• Scikit-Learn

• SQL

• PySpark

Deep Learning Frameworks

• PyTorch

• TensorFlow

• Keras

Production AI & MLOps

• MLOps

• MLflow

• Kubeflow

• Model Registry

• Feature Store Concepts

• Model Monitoring

Containerization & Orchestration

• Docker

• Kubernetes

• Helm

Distributed Processing

• Apache Spark

• Distributed Training Frameworks

• Parallel Data Processing Systems

System Design

• Scalable AI Architecture

• Distributed Systems Design

• Performance Engineering

• Reliability Engineering

• Latency Optimization

• Observability & Monitoring

Good to Have Skills and Experience Required

Advanced AI Infrastructure

• GPU Computing

• CUDA

• NVIDIA RAPIDS

• TensorRT

• Distributed GPU Training

Big Data Ecosystem

• Hadoop

• Kafka

• Flink

• Trino

• MinIO

Platform Engineering

• CI/CD Pipelines

• GitHub Actions

• Jenkins

• ArgoCD

• GitLab CI/CD

Advanced Analytics

• Graph Analytics

• Network Analysis

• Fraud Network Detection

• Entity Resolution

Research & Innovation

• Publications in IEEE, ACM or reputed AI/ML conferences

• Patent filings or innovation contributions

• Experience designing custom loss functions and optimization algorithms

• Experience solving large-scale real-world AI problems