Chief Data Architect

7 days ago

Kottayam, Kerala, India beBeeEntity Full time US$ 2,00,000 - US$ 5,00,000
Lead Data Scientist - Entity Resolution & Network Generation

We are seeking a highly skilled Lead Data Scientist to spearhead the development of our Entity Resolution and Network Generation services. As a key member of our team, you will be responsible for architecting and implementing distributed graph computing solutions that process billions of entities and relationships.

Our cloud-native platform leverages cutting-edge technologies such as Microservices architecture with Kubernetes orchestration, Apache Spark for distributed processing, Elasticsearch for real-time search and fuzzy matching, and Scala as the primary development language. We also employ data mesh principles with API-first design to ensure seamless data integration and exchange.

Key Responsibilities:

  1. Design and implement efficient distributed entity resolution algorithms capable of processing large volumes of records.
  2. Build blocking strategies optimized for Spark at scale.
  3. Develop fuzzy matching algorithms leveraging Elasticsearch's capabilities.
  4. Create machine learning-enhanced matching with explainable AI for match decisions.
  5. Implement incremental resolution supporting real-time and batch modes.
  6. Design APIs for entity lookup with sub-100ms latency requirements.

Network Generation Service:

  1. Architect distributed graph generation pipelines using GraphX/GraphFrames.
  2. Implement graph analytics algorithms such as PageRank, community detection, and centrality measures.
  3. Design storage strategies for multi-billion edge graphs in Parquet/distributed file systems.
  4. Build temporal graph support for time-evolving networks.
  5. Create high-performance graph serving APIs with complex query capabilities.
  6. Optimize graph partitioning to minimize shuffle and maximize locality.

Artificial Intelligence Development:

  1. Develop Graph Neural Networks (GNNs): Build GNN models using PyTorch Geometric or DGL to analyze corporate and transaction networks, detecting fraud rings and risk patterns.
  2. Implement Entity Resolution: Design algorithms for fuzzy matching, semantic matching, and clustering to unify entities across heterogeneous data sources.
  3. Create Risk Scoring Models: Combine rule-based, supervised, and unsupervised methods to generate composite risk scores optimized for real-time and large data processing.
  4. Advance Composite AI: Leverage ContexQ's proprietary approach integrating symbolic AI, vector embeddings, and graph AI for robust entity resolution and network analytics.
  5. Explainable AI (XAI):
  6. Champion Transparency: Integrate SHAP, LIME, and GNNExplainer to provide clear explanations for model predictions.
  7. Ensure Fairness: Audit models for bias and fairness embedding ethical principles into every stage of development.

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