Data Science Cloud Leader

2 days ago


Kolhapur, India beBeeDecision Full time

Cloud Decision Intelligence EngineerTransform financial crime, fraud and risk management through innovative solutions.Distributed graph computing to process billions of entities and relationships for a next-generation data intelligence platform using microservices architecture.Entity resolution and network generation services.The engineer is expected to be performance-obsessed, think in distributed systems, optimize for latency and throughput, and act as a self-directed technical leader who makes architectural decisions and implements them.Design and implement distributed entity resolution algorithms to process billions of records.Build blocking strategies (e.g., LSH, canopy clustering) optimized for Spark at scale.Create AI/ML based advanced matching with explainable AI (XAI).Implement incremental resolution supporting real-time and batch modes.Design APIs for entity lookup with sub-100ms latency requirements.Assist the team in building dynamic entity resolution with embedded graph knowledge of GNN and GT (Graph transformers).Architect distributed graph generation pipelines using libraries such as GraphX/GraphFrames.Implement graph analytics algorithms such as PageRank, community detection, and centrality measures.Design storage strategies for multi-billion edge graphs in the cloudBuild temporal graph support for time-evolving networks.Create high-performance graph serving APIs with complex query capabilities.Optimize graph partitioning to minimize shuffle and maximize locality.Build Graph Neural Networks (GNNs) and models using neighbour sampling techniques such as GraphSAGE to analyse corporate and transaction networks for fraud and risk patterns.Implement AI based Entity Resolution algorithms using techniques such as fuzzy matching, semantic matching (Sentence-BERT), and clustering across heterogeneous data sources.Create Risk Scoring Models by combining rule-based, supervised and unsupervised methods, optimized for real-time and large data processing.Champion model transparency and fairness by integrating state-of-art models evaluation techniques such as SHAP, LIME, bias detection and monitoring model drifts.



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