Data Scientist (Hybrid)

at  Elder Research

SUMMARY OF POSITION:

Supporting DoD’s biosurveillance program and national efforts against chemical, biological, radiological, and nuclear (CBRN) threats as a Data Scientist you will be a part of a team that focuses on gathering, interpreting, automating, standardizing, communicating, documenting, and integrating data into a command data model/data mesh network, surveillance architectures, and test detection systems.
This position will be part of our Defense and Intelligence Team serving the Departments of Defense and Homeland Security as well as the IC as our primary customers.

JOB SPECIFICATIONS/REQUIREMENTS:

• Experience with ML fields, e.g., natural language processing, computer vision, statistical learning theory;
• Experience in an ML engineer or data scientist 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; and
• Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations.
• 5+ years experience developing in Python and SQL; experience with Apache
• 5+ years experience developing dashboards using either Qlik or Tableau
• Minimum 2+ years experience working directly with either contracting or FM
  data sources

DESIRED SKILLS:

• Experience with Spark and EQL is a plus
• Bachelor’s degree plus 1-3 years experience, or a Masters Degree plus 1-3years of experience.

• Develop and maintain dashboards according to user requirements
• Collaborate with data engineering and functional resources to build performant applications to support requirements from the procurement, FM and other communities as needed and approved by leaders
• 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 external stakeholders
• Ability to develop strategic, baselined, data modeling processes; ability to accurately determine cause-and-effect relationships; and
• Experience with integrated development environments, data integration, data visualization, data mining, and analysis tools.
• Maintains and guides the development of common libraries and tools used

by multiple teams.

• Aids in formulating a strategy on how to achieve rapid prototyping