
Senior Data Science Specialist
3 days ago
We are seeking a senior expert to lead the development of our Entity Resolution and Network Generation services. This is a hands-on technical leadership role where you'll architect and implement distributed graph computing solutions processing billions of entities and relationships.
The PlatformOur cloud-native platform leverages microservices architecture with Kubernetes orchestration, Apache Spark for distributed processing, Elasticsearch for real-time search and fuzzy matching, Scala as the primary development language, and data mesh principles with API-first design.
- Entity resolution algorithms
- Blocking strategies optimized for Spark at scale
- Fuzzy matching algorithms leveraging Elasticsearch's capabilities
- ML-enhanced matching with explainable AI for match decisions
- Incremental resolution supporting real-time and batch modes
Entity Resolution Service
- Design and implement distributed entity resolution algorithms capable of processing billions of records
- 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
- 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
Graph Neural Networks (GNNs)
- Develop GNN models using PyTorch Geometric or DGL to analyze corporate and transaction networks, detecting fraud rings and risk patterns
Entity Resolution
- Design algorithms for fuzzy matching, semantic matching (Sentence-BERT), and clustering to unify entities across heterogeneous data sources
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
Explainable AI (XAI)
- Champion transparency by integrating SHAP, LIME, and GNNExplainer to provide clear, interpretable explanations for model predictions
- 7+ years of experience in distributed computing and big data systems
- 5+ years specifically in entity resolution and graph analytics at scale
- Expert-level Scala programming skills
- Deep experience with Apache Spark, including custom optimizations
- Production experience with Elasticsearch for search and matching
- Proven track record building systems processing billions of entities/edges
- Strong understanding of blocking algorithms and their trade-offs
- Experience with probabilistic record linkage and similarity measures
- Expertise in graph algorithms and their distributed implementations
- Knowledge of graph storage formats and query optimization
- Understanding of ML applications in entity resolution
- Experience designing microservices architectures
- Track record of building fault-tolerant, scalable systems
- API design experience with GraphQL or REST
- Performance optimization and capacity planning expertise
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