You will be evaluated for this job based on how well you meet the qualifications above.
Qualified applicants will have a strong technical background in a computational science discipline (e.g., Mathematics, Statistics, Data or Computer Science) and research experience in mathematical analysis of large data sets. Experience in operational areas is a plus.
Exceptional candidates will have experience applying machine learning methods, including but not limited to a subset of deep learning, reinforcement learning, ensemble methods, and large scale graph analytics. Significant programming experience, especially working with large data sets (e.g., Python, Tensorflow, R, Java, C/C++, and/or other data processing frameworks) is preferred.
The ideal candidate is someone with excellent problem-solving, communication, and interpersonal skills, who possesses a range of knowledge and experience with:
- Applying principles and methods of linear algebra (e.g., vector spaces, matrices, matrix manipulations) to solve complex problems;
- Applying the mathematical principles, combinatorial methods or elicitation techniques to determine or calculate the likelihood of outcomes;
- Quantifying the likelihood of an event's occurrence;
- The scientific principles, methods, and processes used to conduct research studies (e.g., study design, data collection and analysis, and reporting results);
- Applying data-analytic techniques to analyze, visualize, and summarize sample data from populations;
- Drawing inferences regarding populations based on results from sample data.
- Concepts and procedures for applying algorithm design techniques (e.g., data structures, dynamic programming, backtracking, heuristics, and modeling) to design correct, efficient, and implementable algorithms for real-world problems;
- Debugging and testing software programs;
- Using best programming practices (e.g., appropriate coding standards, algorithm efficiencies, coding documentation);
- Using principles, techniques, procedures, and tools that facilitate the development of software applications;
- Using software and computer languages and skills (e.g., writing code, debugging/testing programs, fixing syntax, correcting logic errors, using abstract data types) to develop programs that meet technical requirements;