Graph Data Scientist - Level II
Arlington, VA
Applications have closed
Redhorse Corporation is expanding our world-class knowledge graphs team to support a high-priority analytics project. The Graph Data Scientist – Level II will serve on a cross-functional engineering team to quickly establish and continuously improve a knowledge graph-enabled analytics platform to support a focused analytic mission. The Data Scientist will share the responsibility of the analysis of a global-scale knowledge graph comprised of multiple commercial and open sources data sets, supporting human analysis, applied data science, and graph-based machine learning use cases. The work location for this position is in the National Capital Region (NCR) with the possibility of up 25% local travel.
Primary Duties and Responsibilities for this position include:
- Contribute deep expertise and vision to the development of a mission-focused graph-enabled analytics platform, to include knowledge graph development and design, schema development, data science workflow development, machine-aided analytics, and graph-based machine learning initiatives.
- Designs, configures, develops, tests, and supports informatics and data science solutions for a wide array of technical use cases.
- Applies analytical methodologies to diagnose data-related challenges, implement solutions, and evaluate performance.
- Documents and presents requirements, design alternatives, and findings to team members and clients.
- Drive the adoption and optimization of integrated development environments, data integration, data visualization, data mining, and analysis tools
- Mentor and develop junior data scientists and other team members.
Minimum Basic Requirements for Skills, Experience, Education and Credentials include:
- US citizen with a Secret US government clearance. Applicants who are not US Citizens and who do not have a current and active Secret security clearance will not be considered for this role. (TS/SCI clearance, preferred)
- Bachelor’s or Master’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent professional or military experience is required.
- 4+ years of non-internship relevant work experience
- Experience working with large scale knowledge graphs, native graph databases, and knowledge graph applications (e.g. rules engines, cognitive search, complex data exploration and visualization workflows, recommendation engines).
- Experience with machine learning applications, e.g., natural language processing, computer vision, statistical learning theory.
- Experience in data scientist or ML Engineer role building ML models.
- Experience writing code in Python, R, Scala, Java, C++ with documentation for reproducibility.
- Experience handling terabyte size datasets, diving into data to discover hidden patterns, using data visualization tools, writing SQL, and working with GPUs to develop models.
- Familiarity with natural processing language (NLP) models or ML model building.
- Experience with Agile software development practices and tools such as JIRA, Confluence, GitHub, and/or GitLab.
- Experience designing and delivering software solutions in AWS cloud environments.
- Familiarity with containerization tools and techniques, container orchestration, and workflow management, to include technologies such as Docker, Kubernetes, Jenkins, or Terraform is preferred.
- Experience delivering software or technical solutions to the Intelligence Community is preferred.
- Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Agile AWS Computer Science Computer Vision Data Mining Data visualization Docker Engineering GitHub GitLab Jira Kubernetes Machine Learning Mathematics ML models NLP Python R Research Scala Security SQL Statistics Terraform
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