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Direct experience with SQL programming and large database applications such as Oracle, DB2, SQL Server, SPSS, Teradata, and/or Hadoop. Developing and applying statistical or machine learning methods in a corporate environment through applications such as R, Python, or SAS.
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The statistical programmer will be an integral member of the research team and provide programming support for the cleaning, summarizing/reporting, and analysis of research data. The statistical programmer will work most closely with the biostatistician and data managers.
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Develop statistical and advanced analytics models using advanced analytic techniques - Natural Language Processing, Graph, Computer Vision, modeling and simulation, Operations Research, anomaly detection, data visualization and metrics creation.
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Strong experience working with Python or R programming languages. Bachelor's Degree in Computer Science, Engineering, or Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, or related field and 10 years of experience OR a Master's degree and 8 years of experience.
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Recent or soon-to-be completed Ph. D. (typically completed within 3 years) in Computer Science or Mathematics with strong background in one or more of the following: Statistical machine learning, Bayesian deep learning, probabilistic and differentiable programming, probability and measure theory.
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Strong proficiency in programming using Python, R, and SQL. Experience with statistical modeling using large and complex datasets. Model inventory and maintenance: Maintains inventory of all statistical models running in production, with input data, use cases, benefits and risks (data, technical, privacy, regulatory, brand) Actively collaborates with the corporate model risk management team (financial, actuarial, etc.
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Applies intermediate level statistical methods, simulation models, and skilled statistical programming as needed. Work Experience:Five years of experience with mainframe and/or PC databases, document processing, and statistical software such as SAS, R, Stata, SPSS.Preferred Qualifications: Work Experience: 7-10 years of experience with mainframe and/or PC databases, document processing and statistical software.
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Requires two (2) years of experience in each of the following: JIRA; Python, Hadoop and Hive; machine learning, statistical analysis and predictive modeling; SQL programming languages; Visualization tools, including PowerBI and Tableau; Big data platforms including Hadoop (Azure, GCP, or AWS); traditional relational data warehouse technologies: Oracle, Teradata, or DB2; PySpark; and HDFS commands.
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Experience using statistical analysis and computing, machine learning, deep learning, processing large data sets, data visualization, data wrangling, mathematics, and programming.
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Strong Excel skills ideally incorporating VBA (Visual Basic for Applications), programming skills in Python, R or other statistical languages is a plus. To fulfill this role, a risk professional is required who has experience in market risk management and credit risk management areas, or has training in finance, mathematics or other quantitative fields.
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Familiarity with multiple machine learning or statistical packages in Python, R, MATLAB, or another programming language. Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field.
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Bachelor's degree or higher in Statistics, Data Science, Mathematics, Business Analytics, Information System, or closely related field with an emphasis on data visualization tools, SAS programming, research design, data modeling, data mining, and advanced analytics reporting.
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Experience with analytical / statistical programming tools for data extraction and summarization, statistics, and analysis (SAS, SQL, SPSS, R, STATA, MatLab, etc. Employer Consulting Analyst would have a BA/BS degree (or equivalent experience) in social science, public health, economics, statistics, actuarial science or mathematics and 4 years of related work experience or equivalent combination of education and experience.
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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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The day-to-day: As a Data Scientist you will utilize experience in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high level language (e.g. Python), statistical analysis, variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.
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