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Independently perform sophisticated data analytics (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data.
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8 years of hands-on experience developing Statistics, Machine learning, and AI solutions. Oversee and guide a team of professionals to create advanced analytics which will leverage predictive modeling, machine learning, data mining, data visualizations, simulation, and optimization.
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Knowledge of artificial intelligence, machine learning, and data science concepts, methodology and techniques. Bachelor's degree in a relevant field, such as public policy, economics, statistics, business, data science, or industrial/organizational psychology (master's degree in in related field preferred.
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A deep technical background in applied data science, spanning some or all statistics, experimentation, machine learning, causal methods, optimization techniques, data engineering and architecture (applied to large data), and behavioral analytics.
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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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Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. Bachelors/Masters in a quantitative discipline (e.g., AI, statistics, operations research, econometrics, computer science, engineering) with 10+ years (or PhD with 8+ years.
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Utilizes an in-depth knowledge of data science techniques and methodologies such as machine learning, predictive analysis, prescriptive analysis and optimization, and other emerging analytical techniques to assist, consult, or lead others in the application of data science techniques.
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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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Familiar with current and emerging techniques in analytics (machine learning, deep learning, AI) + Strong programming experience with R and/or Python for statistical modeling and other machine learning applications.
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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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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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7 years of hands-on experience developing Statistics, Machine learning, and AI solutionsDemonstrated track record of successfully delivering data-driven solutions that have significantly impacted business outcomes.
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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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The Anti-Money Laundering Modeling and Advanced Data Insights team is on a journey to modernize the way Capital One identifies potential money laundering, terrorist financing, and human trafficking through the use of machine learning, statistics, and other advanced analytic techniques.
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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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