Data Scientist Lead

3 days ago

Bareilly, Uttar Pradesh, India beBeeExpert Full time ₹ 2,00,00,000 - ₹ 2,50,00,000

About the Role

As a senior expert in artificial intelligence and machine learning, you will lead the development of our Entity Resolution and Network Generation services. This is a hands-on technical leadership role where you will architect and implement distributed graph computing solutions to process billions of entities and relationships.

The Platform

  • Microservices architecture with Kubernetes orchestration
  • Apollo Spark for distributed processing
  • Elasticsearch for real-time search and fuzzy matching
  • Scala as the primary development language
  • Data mesh principles with API-first design

Key Responsibilities

Entity Resolution Service
  • Design and implement distributed entity resolution algorithms capable of processing large datasets
  • Build blocking strategies (e.g., LSH, canopy clustering) optimized for Spark at scale
  • Develop fuzzy matching algorithms leveraging Elasticsearch's capabilities
  • Create ML-enhanced matching with explainable AI for match decisions
  • Implement incremental resolution supporting real-time and batch modes
  • Design APIs for entity lookup with sub-100ms latency requirements
Network Generation Service
  • Architect distributed graph generation pipelines using GraphX/GraphFrames
  • Implement graph analytics algorithms (PageRank, community detection, centrality measures)
  • Design storage strategies for multi-billion edge graphs in Parquet/distributed file systems
  • Build 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
AI Model Development
  • Build Graph Neural Networks (GNNs): Develop GNN models (e.g., GraphSAGE, GATv2) using PyTorch Geometric or DGL to analyze corporate and transaction networks, detecting fraud rings and risk patterns.
  • Implement Entity Resolution: Design algorithms for fuzzy matching, semantic matching (Sentence-BERT), and clustering to unify entities across heterogeneous data sources (e.g., CSVs, APIs, PDFs).
  • Create Risk Scoring Models: Combine rule-based, supervised (XGBoost), and unsupervised (Isolation Forest) methods to generate composite risk scores, optimized for real-time and large data processing.
  • Advance Composite AI: Leverage ContexQ's proprietary approach, integrating symbolic AI, vector embeddings, and graph AI for robust entity resolution and network analytics.
Explainable AI (XAI)
  • Champion Transparency: Integrate SHAP, LIME, and GNNExplainer to provide clear, interpretable explanations for model predictions, meeting regulatory and ethical standards.
  • Ensure Fairness: Audit models for bias and fairness, embedding ethical principles into every stage of development.

Additional Responsibilities

  • Ensure seamless integration between entity resolution and network generation
  • Design data lineage tracking across both services
  • Implement comprehensive monitoring and observability
  • Contribute to API design and service contracts
  • Optimize for 10x scale growth

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