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Machine Learning/Data Scientist/Engineer
2 months ago
Machine Learning/Data Scientist/Engineer (Intermediate) - Opening #606
Clearance: Public Trust Required
Location: Washington D.C./Hybrid
Education: Master of Science in Applied/Computational Math, or related Scientific Field
The Intermediate Scientist/Engineer shall have significant demonstrated mid-to-executive-level technical experience with the management of technical projects, or equivalent occupational experience with at least 9 years or more of relevant experience.
Duties:
- Support the execution of research programs
- Design, development, and source selection for new programs
- Work closely with the PM as well as other technical support staff
- Monitoring and evaluating program efforts
- Developing briefing materials, as well as communicating program goals and results to a broad audience.
- Candidates must possess excellent organization and communication skills, strong attention to detail, and ability to handle a wide variety of tasks, including briefing support and general administration.
- Strong technical writing skills and an ability to prepare effective presentations that can communicate complex scientific ideas to broad audiences are required.
- The ideal candidate will be very organized, highly self-motivated, detail oriented, and adept at multitasking with a demonstrated ability to respond quickly to often changing priorities in a high-pressure environment.
- Candidates should be skilled in reaching out and communicating with ARPA-H and other government personnel, digesting basic and applied research and demonstrating a willingness to explore scientific literature on relevant topics.
Required:
- At least 9 years of related experience
- Candidates for this position will have experience in process development and refinement.
- Experience in data science or machine learning is required.
- Master of Science in Applied/Computational Math, or related Scientific Field
Desired:
- Experience in ML/AI models or ML data pipelines is highly preferred.
- Experience with Advanced Research Project Agency (ARPA) model (i.e., DARPA, ARPA-E, IARPA, HSARPA), structure, and processes is preferred.