Lead Data Scientist

Mployee.me; Hyderabad; Vor Ort; INR 3.000.000 - 5.400.000; Vollzeit

Zusammenfassung

NPCI Market Innovation AI Engineering team is seeking a Lead Data Scientist to drive development of next-generation ML/DL systems powering India’s largest digital payments ecosystem. You will design, build, and scale production-grade AI platforms handling billions of transactions with low latency and high reliability.

The role focuses on fraud detection, AML analytics, behavioral intelligence, and real-time decisioning, collaborating with engineering, data platform teams and academia.

Qualifikationen

• Expertise in building ML/DL solutions for large-scale data.
• Experience with fraud detection, risk scoring or anomaly analytics preferred.
• Strong feature engineering and model evaluation skills.
• Experience deploying ML models in production with monitoring.

Aufgaben

• Design, develop, and deploy advanced ML solutions.
• Build predictive models for fraud detection, anomaly detection, and risk scoring.
• Develop robust feature engineering frameworks for large-scale data.
• Design evaluation methodologies aligned with business metrics.
• Build explainable AI systems for regulated environments.

Kenntnisse

Supervised Learning

Unsupervised Learning

Time Series Forecasting

Anomaly Detection

Graph Analytics

Reinforcement Learning

Ausbildung

B.Tech / MSc in CS/AI

Tools

PyTorch

TensorFlow

Keras

PySpark

Kafka

Jobbeschreibung

Job Summary

You will be part of NPCIs 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

• 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 Analytics
• 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.