Senior Data Scientist

National Payment Corporation of India (NPCI)  is hiring!

Senior Data Science (AI/ML Systems)

Location:

Hyderabad
Role Type:  Permanent

Experience:

11 - 15 years

The Opportunity
At NPCI, you will drive the  design and deployment of next‑generation AI and data science systems  that power Indias digital payments ecosystem at population scale. This role goes beyond traditional modelingrequiring deep hands-on expertise in building end-to-end AI/ML pipelines, production-grade platforms, and scalable distributed systems.
You will work with advanced machine learning, deep learning, and LLM-based architectures while leading high-impact initiatives across product, engineering, and regulatory domains. This is an opportunity to build AI systems that operate at national scale with high performance, low latency, and strong reliability.

Job Summary
We are seeking a  Senior Data Scientist  who brings strong hands-on technical depth in AI/ML along with the ability to architect, scale, and lead complex analytical systems. The ideal candidate is an expert in machine learning, deep learning, and large-scale system designcapable of taking AI solutions from concept to production with high engineering rigor.

Key Responsibilities
AI/ML System Design & Development
Design, develop, and deploy  production-grade machine learning and deep learning systems  (Tensor Flow, PyTorch, Keras).
Build and operationalize  supervised and unsupervised learning models , including classical statistical models, tree-based methods, neural networks, and advanced DL architectures.
Architect and optimize  end-to-end ML pipelines  covering data ingestion, feature engineering, training, serving, scaling, and monitoring.
Large-Scale & Distributed Systems
Work with  big data ecosystems  and distributed computing technologies to handle extremely large datasets at high throughput.
Design systems for  high availability, ultra-low latency, observability, and fault tolerance .
MLOps & Deployment
Implement  CI/CD pipelines  for ML workflows.
Work with  Docker, Kubernetes , container orchestration, GPU clusters, and scalable microservice architectures.
Integrate platform components such as  MinIO, Trino, Feature Stores , and distributed caching.
Advanced AI & LLMs
Apply LLMs, transformer models, and  agentic AI systems  where relevant.
Explore  federated AI architectures  for privacy-preserving ML.
Technical Leadership & Collaboration
Mentor data scientists and ML engineers; lead architectural decisions and code reviews.
Work closely with Product, Engineering, Business, and Operations teams to deliver enterprise AI solutions.
Ensure strong alignment with audit, compliance, and regulatory expectations.
Contribute to NPCIs thought leadership via  white papers, conference presentations, and technical publications .

Key Skills & Experience Required
Core Technical Expertise
Strong foundations in  Machine Learning, Deep Learning, Statistics, and Bayesian methods .
Expert-level proficiency in  Python  and strong command of  SQL .
Hands-on experience with  Tensor Flow, PyTorch , and GPU-accelerated computing (CUDA, NVIDIA stack).
Experience working with  large-scale data systems  and distributed storage/compute.
Practical understanding of  feature store architectures  and ML observability frameworks.
Engineering & System Design
Strong knowledge of:
Kubernetes, Docker
CI/CD for ML (Git Ops, Dev Ops, MLOps)
MinIO, Trino/Presto, Object Storage, Data Lakes
Model serving, monitoring, and drift detection
AI at Scale
Experience designing ML systems optimized for  scalability, latency, fault tolerance, performance tuning, and resource management .
Exposure to  federated learning, distributed model training, and agentic AI .

Soft Skills & Leadership
Excellent problem-solving, analytical thinking, and communication skills.
Strong ability to translate ambiguous business problems into scalable AI-driven solutions.
Experience in  stakeholder management  and cross-functional leadership.

Good-to-Have Skills
Exposure to  digital payments, banking, finance , or regulated environments.

Experience with Operations Research tools (Google OR, IBM ILOG).
Published research, white papers, patents, or conference presentations.