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Job Description : We are looking for an associate with at least 5+ years of experience for Sr Statistical programming position, having strong understanding of SAS Base/Macros/GTL and experience of SDTM/ADAM/TLFs creation as per CDISC standards.
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Develop models leveraging statistical analysis, regression modeling, optimization, predictive modeling, simulation, machine learning, economic models. Knowledge of analytical models, methods, applications and tools, such as statistical analysis, regression modeling, predictive modeling, simulation, machine learning and selecting and applying the right analytical techniques.
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Ideally, you'll also have · Master's degree, CPA or CFA preferred · Experience working in a fast-paced mergers and acquisitions environment · Experience analyzing financial statements and key drivers of financial performance · Communicable experience with a subset of advanced analytical methods, such as statistical pricing studies, customer lifetime value analyses, customer persistency/retention analyses, geospatial analyses, statistical clustering analyses, etc.
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Position Requirements PhD in computer science, statistical genetics, bioinformatics or related quantitative subject. The lab is joint between the NYGC and Columbia University Departments of Computer Science and Systems Biology The Azizi lab (azizilab.com) utilizes an interdisciplinary approach combining cutting-edge single-cell genomic and imaging technologies with statistical machine learning techniques, to characterize complex populations of interacting cells in the tumor microenvironment as well as their dysregulated circuitry and spatial organization.
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We are seeking a highly motivated candidate, interested in statistical cancer genomics, for a position as a postdoctoral fellow. Solid methodological training in statistics, be comfortable working with large data sets, proficient in at least one of the statistical programming languages R/Matlab/Python and have experience working on Unix/Linux systems and basic shell scripting.
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The GRR Report Owner of Country Exposure, Legal Entity, and Systemic Risk will be the key point of contact as it relates to providing relevant financial information and statistical data to regulators, responding to inquiries, and having recurring meetings, as requested, involving the Federal Reserve Bank (FRB), the Office of the Comptroller of the Currency (OCC) and the Federal Deposit Insurance Corp (FDIC.
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Experience with statistical modeling software (MATLAB, R, SAS, SPSS, Python) database tools (Amazon Web, SQL, MS Access, Excel PivotTables, MS Sql Server) and enterprise business intelligence tools such as Cognos, Business Objects, Tableau, or Datorama.
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PhD in Statistics, Biostatistics, or related discipline with minimum 7 years of relevant experience in statistical analysis of biomedical data using SAS, R language or equivalent software in the biopharma industry or a related environment.
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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. Approve credit reviews as a first line of defense underwriter (Credit Officer CO/Senior Credit Officer SCO designation) including assessment of portfolio risk, liquidity of client positions, credit limits and risk appetite ratios.
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Use data science methods, including but not limited to Artificial Intelligence/Machine Learning (AI/ML) approaches, to perform data processing (e.g. filtering), feature recognition, alarm threshold determination, pattern matching, statistical trending, scoring of detection quality, and integration of other information into decision making.
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Advanced knowledge of Excel and extensive experience with and proficiency in statistical computing software or data analytic environments (e.g., SAS, SPSS, Stata, SQL/NoSQL databases including ETL, Python, R, Tableau), including the ability to analyze large and complex datasets for patterns and trends, and build statistical models.
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Expert ability in operations research and statistical methods and techniques, including deployment analysis and resource allocation modeling, systems/operations analysis, simulation and optimization (linear and nonlinear), predictive analytics (e.g. deep learning), econometrics, time series/forecasting, vehicle routing and related skills.
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In-depth knowledge of statistics, proficiency in statistical analysis software (e.g., Minitab, JMP) and ability to make data-driven decisions and recommendations. Our products are used in a wide variety of industries including building products, aerospace, automotive and transportation, filtration, commercial interiors, waterproofing and wind energy.
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5 years of experience must include: Data analytics and statistical analysis of financial risk data; Policy and regulatory analysis; Data analytics tools including: R, Excel, and PL/SQL; Anti-Money Laundering (AML) and Know Your Client (KYC) guidelines; Oracle and relational databases; and User Acceptance Testing (UAT) and defect management.
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Use data analytics and statistical analysis to develop and assess customer risk scoring methodology, monitor trending of risk distribution, and design testing scenarios to ensure that logics are implemented correctly in relevant Anti Money Laundering (AML) and Know Your Customer (KYC) applications.
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statistical job in New York, NY
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