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Full understanding and experience working with various technologies to include Python coding, JSON files, Zeek data, Cloud, Azure, AWS, and Artificial Intelligence/Machine Learning (AI/ML.
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We are seeking a Data Science Fellow, to develop essential skills in healthcare analytics and modern methods in artificial intelligence/machine learning to advance research in precision medicine.
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Bachelors/Masters in a quantitative discipline (e.g., AI, statistics, operations research, econometrics, computer science, engineering) with 10+ years (or PhD with 8+ years). Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
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Bachelors degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics, or related Quantitative disciplines and 5+ years experience in predictive analytics or equivalent field.
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Experience with data modeling, machine learning algorithms, optimization, and data science techniques e.g. decision trees, SVM, neural networks, time series, forecasting, classification, regression, clustering, cross-validation, and model evaluation.
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BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related fields and minimum 4 years related business experience in analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role OR advanced degree and 2 years directly related experience.
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Experience in mathematics (statistics, linear algebra, differential calculus); data visualization (Tableau, Power BI, Qlikview); programming (SQL, Python, R, Java); data analysis (feature engineering, data wrangling, EDA) and machine learning (classification, regression, reinforcement learning, deep learning, clustering, dimensionality reduction.
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Bachelors degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field and minimum 8 years of related experience, of which at least 4 years of directly related data analytics, data science, predictive modeling, machine learning, statistical modeling experience OR advanced in the required fields and 4 years directly related data science, analytics experience.
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Master’s degree or PhD in Engineering, Statistics, Machine Learning, Computer Science, Econometrics, or other quantitative fields. 7+ years of professional experience in Data Science, Machine Learning and Credit Risk or Fraud Strategies.
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But is not limited to—statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods/modeling.
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Integrating innovative approaches in individual and team work products such as machine learning, artificial intelligence, data mining, predictive modeling, neural networks and simulation to mitigate price/value leakage across the pursuit and delivery phases of an engagement.
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For these openings, preference will be given to qualified applicants who can teach classes in one or more of the following areas: undergraduate programming and data structures; computer engineering; database and related; data science, and AI/machine learning.
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Emphases will be on the applications of machine learning and artificial intelligence techniques for solution-driven applications in genomics, phenomics, high-throughput above-ground and below-ground phenotyping, agronomy, soil science, climate-smart agriculture, microbiome dynamics, or similar.
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Evidence of commitment to undergraduate and graduate education and ability to teach in one or more of the broad topical areas in electrical and computer engineering: microelectronics, power engineering, cybersecurity, machine learning/AI, data science, wireless communications, high performance computing, quantum information sciences and engineering.
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Hydroinformatics - Motivated by the expansion of data acquisition systems and the need for managing and using massive datasets, this position requires specialization in information systems and technology for water management, engineering, and operations, with expertise in extreme data management, analytics, data mining, large dataset access, remote sensing, artificial intelligence, machine learning, water systems modeling, and geographic information systems.
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