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A graduate degree in Operations Research, Financial Engineering, Physics, Mathematics, Statistics or Computer Science. Strong machine learning and statistical analysis skills. Any interest in the cryptocurrency space will be highly regarded.
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Develop analytical workflows for statistical and functional analysis of large-scale proteomics data. Proficiency in Python and/or R. Hands-on skills using data science packages. Deep understanding of data generation, raw data analysis, as well as statistical and bioinformatic analysis of processed data to derive biologically meaningful insights.
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Will also influence and supply to strategy development, participate in regulatory and routine safety submission activities, and represent GBS (Global Biostatistics Science) through participating in and collaborating with multidisciplinary project teams.
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Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Data Science: ML Algorithms, model evaluation & validation, feature generation, data visualization, statistical analysis. Experience in modern scaled cloud platforms like Google Cloud, AWS, Azure, IBM, Alibaba, Oracle, etc.
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PhD or Master’s degree (PhD preferred) in Psychology, Behavioral Science/Economics, Cognitive Science, Computer Science, Machine Learning, Statistics, Social Science, Human-Computer Interaction or related field.
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Preferred Qualifications: A masters degree in a related field (biostatistics, epidemiology, public health, data science) or commensurate experience Skill and experience in Machine Learning and other AI methods Familiarity with commonly used community health metrics Familiarity with U.S. administrative geographies (counties, congressional districts, consolidated and incorporated places, census tracts etc.
$184,500 - $235,000 a yearFull-timeExpandApply NowActive JobUpdated 17 days ago - UpvoteDownvoteShare Job
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Minimum of 2 years of hands-on SAS, Python, R and/or SQL or demonstrated combination of education and experience (OR) Master’s and academic projects, internships, and/or assistantships utilizing statistical programming.
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Experience with programming and statistical packages (Python, R). Bachelor's degree, preferably in Statistics, Mathematics, Actuarial Science, Analytics, Engineering or Computer Science.
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Advanced degree such as a Master's or PhD in Human Factors, Human Computer Interaction, Science, Engineering, Cognitive Science, or related quantitative STEAM field. Bachelor's degree in Human Factors, Human Computer Interaction, Science, Engineering, Cognitive Science, or related quantitative STEAM field.
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This position supports statistical programming activities for late stage drug/vaccine clinical development projects. Ability to complete statistical programming deliverables through the use of global outsource partner programming staff.
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The Data Scientist should be skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as R and Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
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Proficiency in Microsoft Office (Excel, Word, Powerpoint, Outlook) as well as statistical software (Minitab, JMP, etc.) Drive improvement efforts through a solid understanding and application of statistical tools and interpretation of results for effective solutions (i.e., Design of Experiments, process mapping, process capability analysis, Pugh matrix, FMEA, MSA, control plans, etc.
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For this role, Cadmus seeks candidates with professional experience in program evaluation, social science research methods, and/or energy efficiency or sustainability. + Eight to ten years of experience conducting social science research, including interviews, surveys, focus groups, and literature reviews, and/or designing logic models.
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Perform data exploration using a combination of statistical programming languages (including, but not limited R, Python, SQL, SAS) and deploy predictive analytics and machine learning techniques to improve risk prediction, improve reserve, trend and financial forecasting in a manner that is actuarially sound, and enable real-time results and operational efficiencies.
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