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Popular mid-level Data Scientist role in Bangalore with remote option and broad specialized requirements driving high competition.
Strong banking fraud, regulatory, graph analytics, and LLM specialization makes cross-industry transferability limited.
Multiple mandatory technical and domain requirements including 4+ years, fraud banking expertise, LLM and MLOps experience.
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Develop and maintain advanced fraud detection and credit risk models using supervised and unsupervised techniques integrating near-real-time scoring with banking event streams.
Design and fine-tune large language models and generative AI systems (LLMs, RAG) for banking-specific tasks such as SAR generation, dispute summarization, and customer interactions.
Operationalize AI/ML models with production-ready code, MLOps tools, and expose services via microservices within Agile teams; ensure compliance with regulatory requirements and Responsible AI principles.
Minimum 4 years of experience in data science with focus on fraud and banking analytics.
Proven expertise in fraud modeling, graph analytics for fraud network detection, and banking data schemas (ISO 8583, SWIFT, CIBIL/Experian).
Strong skills in AI/ML techniques including supervised (XGBoost, neural networks) and unsupervised models, plus deep learning frameworks (PyTorch or TensorFlow).
Hands-on experience with Python programming, cloud platforms (GCP, AWS or Azure), SQL, and MLOps tooling (MLflow, Kubeflow, Vertex AI Pipelines).
Experienced working in Agile delivery squads with ability to handle end-to-end model development, deployment, and stakeholder engagement in regulated banking environments.
Strong understanding of regulatory frameworks (RBI, FATF AML/CFT, PCI-DSS) and ability to translate complex model behavior for non-technical stakeholders including compliance and risk teams.
Demonstrated capability in integrating cutting-edge generative AI techniques (PEFT, LoRA, RAG, prompt engineering) into practical banking fraud detection and customer communication solutions.