Associate Data Science

Job Title: Data Scientist – AI Engineer

Division: NPCI Data Analytics – Market Innovation

Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field

Employment Type: Full-time

Location: Hyderabad

Role Type: Permanent

Develop and deploy ML/DL models (Logistic Regression, RF, XGBoost, NN, CNN, Transformers, GANs)

Build models for fraud detection, AML, anomaly detection, transaction intelligence

Work on imbalanced datasets using advanced sampling and cost-sensitive learning

Design Graph AI models : GNN, GCN, GAT, temporal graph networks

Apply network analytics for fraud rings, mule detection, behavioral risk signals

Build LLM-powered applications (chatbots, complaint intelligence, document analysis)

Implement: RAG pipelines Agentic workflows & MCP (Model Context Protocols) Prompt engineering & LLM fine-tuning

RAG pipelines

Agentic workflows & MCP (Model Context Protocols)

Prompt engineering & LLM fine-tuning

Perform EDA, feature engineering (temporal, behavioral, aggregated features)

Work with structured, semi-structured, and unstructured data

Optimize models for: Latency & throughput GPU performance (CUDA-based optimization)

Latency & throughput

GPU performance (CUDA-based optimization)

Use libraries such as: RAPIDS, cuDF, cuML, cuGraph, PyTorch Geometric

RAPIDS, cuDF, cuML, cuGraph, PyTorch Geometric

Design custom loss functions (weighted BCE, cost-sensitive)

Apply business-aligned metrics : Precision@K, Recall, ROC-AUC, PR-AUC

Precision@K, Recall, ROC-AUC, PR-AUC

Use robust validation techniques (cross-validation, time-based splits)

Integrate models into batch and real-time production systems

Design scalable ML pipelines & APIs

Monitor: Model drift Performance stability Business impact

Model drift

Performance stability

Business impact

Work with data engineers, product teams, and business stakeholders

Contribute to research, innovation, and academic collaborations

Stay updated on latest AI advancements (LLMs, Graph AI, Federated Learning)

Strong in: Supervised & unsupervised learning Statistical modeling (Logistic Regression, DA) Tree models (RF, XGBoost, LightGBM)

Supervised & unsupervised learning

Statistical modeling (Logistic Regression, DA)

Tree models (RF, XGBoost, LightGBM)

Deep Learning: NN, CNN, Transformers, GANs

NN, CNN, Transformers, GANs

Hands-on experience with: LLMs (OpenAI, open-source models) Prompt engineering, fine-tuning RAG pipelines & vector databases Agent frameworks & MCPs

LLMs (OpenAI, open-source models)

Prompt engineering, fine-tuning

RAG pipelines & vector databases

Agent frameworks & MCPs

Experience with: GNN, GCN, GAT Graph-based fraud detection Network analytics

GNN, GCN, GAT

Graph-based fraud detection

Network analytics

Strong proficiency in: Python (NumPy, Pandas, scikit-learn) SQL (large-scale data processing)

Python (NumPy, Pandas, scikit-learn)

SQL (large-scale data processing)

Frameworks: PyTorch / TensorFlow PyTorch Geometric

PyTorch / TensorFlow

PyTorch Geometric