CERT – Machine Learning Research Scientist (Pittsburgh, PA or Arlington, VA)
A small but growing team of data-centric researchers seeks an applied statistician / data scientist to work on established projects as well as develop new ones. Examples of current projects include developing metrics and experimental designs for large-scale cybersecurity research programs, researching human-in-the-loop machine learning, and performing both exploratory and automated analysis of large corpora of cybersecurity incident data. Though you may encounter big data problems in this position, we find that many of our most interesting challenges currently stem from data quality issues and limited sample sizes. You will have the opportunity to apply, learn, and develop new technical approaches.
You will be expected to work with teams of cybersecurity domain experts as well as other statisticians, and needn’t have previous cybersecurity experience of your own. Explicitly, you will be expected to co-author research proposals and execute applied research (i.e., design research studies and study materials, collect and analyze data, author publications, and present findings to DoD sponsors and academic conferences).
Minimum Qualifications and Requirements
Education/Training: Bachelor’s degree and an academic background in machine learning, statistics, or other related quantitative field with eight (8) years of experience; Master’s degree and an academic background in machine learning, statistics, or other related quantitative field with five (5) years of experience; PhD and an academic background in machine learning, statistics, or other related quantitative field with two (2) years of experience; or equivalent combination of training and experience. Candidates without a PhD should instead have experience demonstrating their knowledge of statistical theory and ability to perform research.
Experience: Two plus (2+) years of experience using statistical methods.
Skills/Abilities: An ideal candidate will have expertise in the following areas. Experience with specific tools and methods are less important to us than evidence that you can learn new tools and methods.
- Design quantitative metrics with real-world utility and validity.
- Apply a wide range of analysis techniques to diverse, potentially underspecified real problems.
- Find, read about and evaluate theoretical results as needed.
- Execute experimental design basics.
- Advise on the feasibility, needs, and design of the data-centered component of new project proposals.
- Design and evaluate data collection strategies aligned to project goals.
Hands-on data analysis:
- Analyze data in R, Python or similar data analysis ecosystem.
- Comfortably use tools for reproducible, documented data analysis.
- Rapidly clean, refactor, explore, model, plot, and merge messy raw datasets.
Collaboration: – Work closely with subject-matter experts. – Communicate with people in other fields about technical statistical concepts.
Other: Candidates will be subject to a background check and must be eligible to obtain and maintain a Department of Defense security clearance.
Preferred Qualifications and Requirements:
Education/Training: PhD in machine learning statistics, or other related quantitative field.
Experience: Five plus (5+) years of experience in statistics or machine learning.
- Strong software engineering skills
- Cybersecurity experience
- Experience supporting test and evaluation for large-scale government research programs.
U.S. citizenship required; must be able to obtain and maintain a Department of Defense security clearance.
Carnegie Mellon University is an EEO/Affirmative Action Employer – M/F/Disability/Veteran
Source: CERT – Machine Learning Research Scientist (Pittsburgh, PA or Arlington, VA)
Via: Google Alert for ML