Graph Neural Networks Data Jobs

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4d ago

Senior Data Scientist

Leidos Bethesda, MD

Bethesda, MD

4d ago

$108k - $195k/yr Full time Senior Analytics Big Data

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Job Description

The Senior Data Scientist will support the NDOC DOMEX Technology Platform, focusing on building data intelligence pipelines, graph and Elasticsearch based analytics, and collaboration to advance a future-state DOMEX Data Discovery Platform for national security.

Location

Bethesda, MD (onsite)

Compensation

Salary range: USD 107,900 - 195,050 per year

Requirements

Education: Master’s degree in Computer Science, Data Science, or a related field; experience: 6–10 years with a Master’s degree, or 8–12 years with a Bachelor’s degree.
Active Top Secret/SCI clearance with the ability to obtain and maintain a Polygraph.
Proficiency in Elasticsearch/OpenSearch architecture, index design, mappings, analyzers, and query optimization.
Experience with graph databases such as JanusGraph, Neo4j, TigerGraph, Amazon Neptune, or Memgraph.
Knowledge of graph analytics frameworks and algorithms, including traversal, centrality, community detection, similarity analysis, and link prediction.
Experience training Neural Networks, including data selection for training, evaluation, and testing.
Experience constructing static and interactive visualizations and dashboards, including link charts.
Proficiency in Python.

Responsibilities

Analyze large data sets across diverse structures to identify patterns and develop dashboards that initiate discovery and present results.
Utilize Elasticsearch/OpenSearch and graph-based platforms to uncover and analyze complex data relationships.
Explore embedding space solutions to enhance search and discovery over structured datasets such as source code repositories and document references.
Investigate graph-structure analysis to reveal intricate network meta-paths and meta-structures across heterogeneous graphs.
Design statistical approaches to manage near-duplicate data and uncertain relationships.
Respond to Government Data Science requests to discover and report insights within graph representations, including custom dashboards, query optimization, and transforming datasets into canonical graph structures.
Design and optimize Elasticsearch indices to efficiently support large-scale hierarchical classification structures with high-performance search, aggregation, and updates.
Assist data engineering in designing, implementing, and maintaining a graph data store optimized for large, dense batch updates and high-throughput analytics.
Prototype ingestion pipelines that enable scalable graph analytics.
Contribute to data schemas, indexing strategies, and partitioning approaches to maximize query performance, scalability, and storage efficiency.
Collaborate with infrastructure and platform engineering teams to deploy, operate, and optimize Elasticsearch and graph database services in Kubernetes using cloud-native technologies.
Partner with Data Scientists, Data Engineers, and AI/ML Engineers to ingest, curate, manage, analyze, and securely expunge datasets throughout the data lifecycle.
Develop automated data loading, transformation, validation, and quality assurance pipelines supporting production analytics workflows.
Optimize graph and search infrastructure for high availability, resilience, and performance under large-scale analytical workloads.
Work with software engineers and system architects to integrate graph and search services into microservice based applications.
Participate in SAFe Agile development activities, including sprint planning, design reviews, architecture discussions, and technical demonstrations.
Foster a culture of innovation, collaboration, and professional development within the team.
Ensure sound engineering practices, policy compliance, and delivery of high-quality software.
Coordinate with test teams to develop and monitor automated system integration tests.
Engage with cross-functional teams to identify and develop high-value integrations with other systems and applications.
Provide input to software components of system design, including hardware/software trade-offs, software reuse, use of COTS/GOTS, and requirements analysis from system level to individual components.

Technologies

Elasticsearch
OpenSearch
JanusGraph
Neo4j
TigerGraph
Amazon Neptune
Memgraph
Kubernetes
Python
Linux
CI/CD pipelines
Graph Neural Networks (GCN, GTNs)

Benefits

Health and Wellness programs
Income Protection
Paid Leave
Retirement

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