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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Nabout Leidos - **Location:** United States - **Experience:** Expert - **Salary:** $107,900.0 - $195,050.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Artificial Intelligence, Artificial Neural Networks, Code Reuse, Information Engineering, Extract Transform Load (ETL), Data Stores, Linux, Elasticsearch, Graph Database, Python (Programming Language), Neo4j, Scrum Methodology, Query Optimization, Data Ingestion, System Availability, Database Optimization, Containerization, Integration Tests, Kubernetes, Information Technology, Data Selection - **Published:** July 28, 2026 - **Apply:** https://jobs.military.com/career/293240/senior-data-scientist-maryland-md-bethesda ## About the Role * Bachelor's degree in Computer Science, Data Science, or related field and 8-12 years of relevant experience, or Master's with 6-10 years of experience.\n * Must possess an Active Top Secret/SCI clearance and ability to obtain and maintain a Polygraph.\n * Elasticsearch/OpenSearch architecture, index design, mappings, analyzers, and query optimization.\n * Graph databases such as JanusGraph, Neo4j, TigerGraph, Amazon Neptune, or Memgraph.\n * Graph analytics frameworks and algorithms, including graph traversal, centrality, community detection, similarity analysis, and link prediction.\n * Experience training Neural Networks including data selection for train, evaluation and test.\n * Experience with constructing static and interactive visualizations and dashboards including link charts.\n * Proficient in Python.\n, * Experience with Linux, containerization, CI/CD pipelines, and Infrastructure-as-Code.\n * Familiar with Graph Neural Networks including GCN and GTNs.\n * Experience with Anomaly Detection models.\n ## Description * Support the design and optimization Elasticsearch indices to efficiently manage large-scale hierarchical classification structures, ensuring high-performance search, aggregation, and update operations.\n * Support data engineering in the design, implementation, and maintenance of a graph data store supporting hierarchical graph structures optimized for large, dense batch updates and high-throughput analytics.\n * Prototype ingestion pipelines that support scalable graph analytics.\n * Support data engineering in implementations of data schemas, indexing strategies, and partitioning approaches to maximize query performance, scalability, and storage efficiency.\n * Collaborate with infrastructure and platform engineering teams to deploy, operate, and optimize Elasticsearch and graph database services in a Kubernetes environment using cloud-native technologies.\n * Partner with Data Scientists, Data Engineers and AI/ML Engineers to ingest, curate, manage, analyze, and securely expunge datasets throughout the data lifecycle.\n * Develop automated data loading, transformation, validation, and quality assurance pipelines supporting production analytics workflows.\n * Optimize graph and search infrastructure for high availability, resilience, and performance under large-scale analytical workloads.\n * Collaborate with software engineers and system architects to integrate graph and search services into microservice-based applications.\n * Participate in SAFe Agile development activities, including sprint planning, design reviews, architecture discussions, and technical demonstrations.\n * Foster a culture of innovation, collaboration, and professional development within the team.\n * Ensure sound engineering practices, compliance with policies, and delivery of high-quality software.\n * Coordinate with test teams to develop and monitor automated system integration tests.\n * Engage with cross-functional teams to identify and develop high-value integrations with other systems/applications.\n * Provide specific input to the software components of system design to include hardware/software trade-offs, software reuse, use of COTS/GOTS in place of new development, and requirements analysis and synthesis from system level to individual software components.\n ## Related Videos - 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