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