ML Engineer
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Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, recommendations and optimization algorithms.
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Experience in delivering world-class data science outcomes, solving complex analytical problems using quantitative approaches with unique blend of analytical, mathematical and technical skills.
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Passion about asking and answering questions in large datasets, and ability to communicate that passion to product managers and engineers.
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Very good applied statistics and mathematics skills
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Data oriented personality
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Great communication skills
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Comfortable in working in international teams, virtual and cross-functional
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Certifying MOOCS (Coursera, Stanford, etc...) can be a complement
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Solid knowledge in statistics: descriptive analysis (Student's test, Fisher test, ANOVA, Chi2, etc…), supervised and unsupervised analysis (regressions, CAH, PCAn, K-Means, decision trees, etc...)
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Machine Learning: linear and logistic regressions, discriminant analysis, bagging, boosting, random forests, gradient boosting, neural networks, text mining and topics extraction, etc.
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Deep Knowledge and experience with common data science toolkits and scripting languages: R , Python (sklearn, panda, numpy,..) or Spark (Mlib) data science libraries
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Experience with common data science toolkits and scripting languages
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Ability to investigate issues and come up with resolutions for large data sets a must
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Ability to work independently and be a self-starter
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Excellent organizational and communication skills
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Fluent in English (written and verbal).
Nice to have:
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Agile best practices experienced
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Cloud basic concepts understanding
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Bachelor’s degree minimum, Master and PhD Degree considered in priority – Mathematics, Statistic, Machine Learning, Data Science, Computer Science, Physics
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5+ years of experience applying statistical concepts and methods, including predictive techniques from data source.