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Working knowledge of statistics, programing and predictive modeling. data mining, advanced statistical analysis (e.g., statistical foundations of machine learning, statistical approaches to missing data, time series), advanced mathematical foundations, artificial intelligence, workflow and reproducibility, data management and curation, data modeling and assessment (e.g., model selection, evaluation, and sensitivity analysis), experience as a data scientist working to support a single or multiple domain area, and/or software engineering.
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Requires BS/BA degree in Mathematics, Statistics, or related field and a minimum of 3 years of experience in broad-based analytical, managed care payor or provider environment as well as experience in statistical analysis and healthcare modeling; or any combination of education and experience which would provide an equivalent background.
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Experience or exposure related to the following topic areas (note that experience in all these areas is not required) Topic Areas: Field work, traffic operations analysis, signal design, transportation planning (e.g., bicycle or transit plans, infrastructure finance, transportation and land use policy, and/or travel demand modeling.
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Develops creative data-driven solutions by employing data science techniques such as operations research, advanced statistics, feature engineering, machine learning/AI, predictive analytics, Master’s degree in data science, operations research, statistics, or a relevant technical field (Predictive Analytics, Computer Science, Mathematics, etc.
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Coursework in transportation, engineering, urban and regional planning, geography, math, statistics, economics, or other related courses (an EIT for engineering candidates is preferred) Completed bachelor’sor in-progressmaster’s degree in transportation, civil or electrical engineering, urban and regional planning, or a related major (e.g.,geography, math, statistics, economics.
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Intermediate to advanced experience in AI/ML modeling in Python/R. Bachelor's degree in Statistics, Mathematics, Operational Research, Computer Science/Engineering, Data Science or other quantitative major.
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Expertise in data mining, ML codebases, ML task development or automation, modeling, forecasting using advanced statistics, analyzing trends and patterns. Knowledge of platform memory subsystem RAS features and memory fault types, from bare metal to OS level transactions.
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BS/MS degree in Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields, with 3+ years of related experience. Experience with user modeling using deep learning methods from large scale datasets.
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As an applied scientist, you will bring statistical modeling and machine learning advancements to analyze data and develop customer-facing solutions in complex industrial settings. Amazon’s CBA team is looking for Applied Scientists, who can work at the intersection of machine learning, statistics and economics; and leverage the power of big data to solve complex problems like long-term causal effect estimation.
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Bachelors degree in Mathematics, Statistics, Engineering, Social/Physical/Life Science, Business, or related field and Minimum five (5) years experience in data analytics or a directly related field.
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Experience in international financial reporting standards, predictive modeling, capital modeling, project management, leading and developing team members, leveraging analytics, and developing solutions beyond traditional actuarial work.
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Solid practical experience in building AI/ML models, statistics, predictive modeling, time series forecasting, regression, classification, clustering, outlier analysis, hypothesis validation, etc.
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In addition, the U of U Health and HCI are home to superb shared resources, such as the Utah Population Database with more than 16 million records linking genealogies, health records, and vital statistics, as well as the Utah Cancer Registry which is a long-standing and preeminent SEER site.
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Proven research track record: publication of peer-reviewed results in the field of AI/algorithms/LLM/IR/databases/statistics and related fields. Knowledge / experience in Large Language Modeling (LLM.
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Knowledge of data mining and analytic methods such as Clustering / Cluster Analysis, Factor Analysis, Regression Models, Logistic Regression, Predictive Modeling, optimization models, etc. An undergraduate degree in a quantitative discipline (Economics, Statistics, Engineering, Applied Mathematics.
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