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 prospect 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

📌 Lead Data Scientist (Hyderabad)
🏢 National Payments Corporation Of India (NPCI)
📍 Hyderabad