FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in statistical learning theory, generalization bounds, and deep neural networks, with a strong ability to apply advanced probability and stochastic processes to real-world scenarios. Proficient in Python programming for developing reproducible code and conducting rigorous literature reviews to support AI certification strategies.
Highest-signal resume keywords
Statistical Learning TheoryDeep Neural NetworksPython ProgrammingAdvanced ProbabilityStochastic Processes
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Statistical ModelingMathematical FoundationsLiterature ReviewMathematical ProofsAlgorithmic AssumptionsTechnical JustificationsPerformance MetricsModel EvaluationsSafety AuditsHypothesis Testing
Soft Skills
Critical ThinkingHealthy SkepticismLogical Argument Construction
Certifications & Qualifications
Master's DegreePhD
Industry Keywords
AI CertificationEASA W-Shaped AI CertificationEnvironmental UncertaintySensor NoiseEmpirical AccuracyQuantitative Research
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Analyze the existing AI certification strategy and adapt or develop strategies for certifying supervised ML
- Align mathematical guarantees, technical arguments, and model evaluations with the EASA W-shaped AI certification process
- Research statistical learning theory, generalization bounds, and mathematical guarantees for deep neural networks in dynamic real-world conditions
- Apply statistics and stochastic processes to model environmental uncertainty and sensor noise and quantify confidence in performance metrics
- Evaluate academic literature, mathematical proofs, and engineering assumptions with peer-review rigor
- Construct technical justifications and logical arguments for safety audits and certification frameworks
- Write clean, reproducible Python code to test hypotheses, validate certification mechanisms, and benchmark model safety frameworks
Requirements
What you’ll need- Deep understanding of statistical learning theory, generalization bounds, and the mathematical foundations of deep neural networks
- Strong background in advanced probability, statistical modeling, and stochastic processes
- Proficiency in Python with ability to write clean, reproducible code; basic Python coding may be learned on the job with strong theoretical foundations
- Capability or background in comprehensive literature reviews, identifying gaps in state-of-the-art AI, and synthesizing academic concepts into engineering frameworks; may be learned on the job with a strong quantitative baseline
- Ability or experience in peer review, including critically evaluating mathematical proofs, algorithmic assumptions, and research papers
- Currently pursuing or recently completed a Master's or PhD in Computer Science, Mathematics, Theoretical Statistics, Electrical Engineering, Physics, or a related quantitative research field
- Strong critical thinking and healthy skepticism toward ML assumptions and empirical accuracy
- Exceptional ability to construct robust logical arguments and technical justifications
- Motivation to solve mathematical problems at the intersection of theoretical computer science, safety engineering, and real-world flight operations