Research Scientist Lead

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: 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
• 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.

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