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דאטה

Deep learning research engineer

145774

Requirements
?  5 years of relevant work experience in data analysis or related field (e.g., as a statistician / data scientist).
? B.Sc. degree in a quantitative discipline (e.g., statistics, bioinformatics, economics, computer science, mathematics, physics, electrical engineering, industrial engineering) or equivalent practical experience.
?     Experience with statistical software (e.g., R, Python, MATLAB) and database languages (e.g., SQL).
?     Independent self-starter and team player
?     Passionate about constantly learning new skills in a fast-paced environment
Preferred qualifications
?     8 years of directly relevant, tech industry work experience, including deep expertise and experience with statistical data analysis such as linear models, multivariate analysis, deep learning, Bayesian inference.
?     M.Sc/PhD degree in a quantitative discipline
?     Applied experience with machine learning and deep learning on very large data sets.
?     Experience articulating cyber security goals and using mathematical techniques to arrive at an answer using available data. Experience translating analysis results into actionable information.
?     Demonstrated skills in selecting the right statistical tools given a data analysis problem. Demonstrated effective written and verbal communication skills.
?    Competitive Programmer or Competitive Data Scientist
?    Knowledge of scalable data visualization techniques
?    Ability to create prototypes quickly
 
Responsibilities:
? Work with large, complex datasets of various vehicular subsystems; solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverable and presentations.
? Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive understanding of vehicular systems and data and offer security-focused insights regarding anomalies in the data sets.
 
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