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Preferred Qualifications for Principal Quantum Modeling Physicist:PhD in theoretical physics, applied mathematics, or a relevant engineering field. Preferred Qualifications for Sr. Principal Quantum Modeling Physicist:PhD in theoretical physics, applied mathematics, or a relevant engineering field plus 4 years of relevant professional experience.
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As an Associate in our Risk Analytics and Modeling team, you’ll have the opportunity to apply your quantitative, modeling and analytical skills in a real world environment to develop or validate statistical, financial engineering, and AI/machine learning models in the areas of credit risk, market risk, assets and liabilities management, fraud detection, anti money laundering and other functional modeling and analytics area.
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Displaying understanding of advanced AI/machine learning, statistical/econometric modeling techniques, and financial engineering methods to develop and/or validate models (Masters-level coursework in at least one of these subject areas is preferred.
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Economics, Statistics, Applied Mathematics, Mathematics, Financial Mathematics, Data Processing/Analytics/Science. Demonstrating exposure to modeling methodologies in at least one of the following areas: credit risk, market risk, assets and liabilities management, fraud detection, anti money laundering and other functional modeling and analytics areas; and.
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Displaying knowledge or familiarity with industry financial instrument pricing tools and data sources e.g. Bloomberg, Refinitiv etc. C#, SQL, Tableau, Alteryx, and Excel Macros / VBA /JS; Possessing experience with one or more of the following key programming and data analytics tools: Python, R, SAS.
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A career in our Advisory Acceleration Centre is the natural extension of PwC’s leading class global delivery capabilities. To help us achieve this we have the PwC Professional; our global leadership development framework.
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Utilize techniques such as Six Sigma, Applied and Advanced Statistics, Predictive/Proactive Analysis & Tools, RCM, FMEA, FEMECA, WCM Advanced Tools, Weibull Analysis, Analytics tools, artificial intelligence, neural networks, and machine learning for innovation.
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As a Senior Associate in our Risk Analytics and Modeling team, you’ll have the opportunity to apply your quantitative, modeling and analytical skills in a real world environment to develop or validate statistical, financial engineering, and AI/machine learning models in the areas of credit risk, market risk, assets and liabilities management, fraud detection, anti money laundering and other functional modeling and analytics area.
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Applying techniques across one or more machine learning models including: multivariate regressions, Bayesian probabilities, clustering algorithms, machine learning, dynamic programming, stochastic-processes, queuing theory, algorithmic knowledge.
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Experience with machine learning methods and frameworks especially applied to physical science problems. The Scientific Software Engineering & Data Management Group in the X-Ray Science Division (XSD) at the Advanced Photon Source (APS) ( invites applicants for a postdoctoral position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments.
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Demonstrated professional experience in advanced Data Science domains such as predictive modeling, Large Language Models, machine learning, text mining, and/or data engineering.
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Python, Java or Perl- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree- 5+ years’ experience with modeling languages and tools like PyTorch / TensorFlow, R, scikit-learn, numpy, scipy, etc.
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Education:Masters degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5 - 7 years experience in data sciences. Experience in Supervised and Unsupervised Machine Learning including classification, forecasting, anomaly detection, pattern recognition using variety of techniques such as decision trees, regressions, ensemble methods and boosting algorithms.
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Experience with solving and using machine learning for real world problems involving scientific/engineering simulations (domains/applications - industrial, life sciences, high energy physics, earth sciences – seismic, weather & climate modeling; physics types - CFD, structural, electromagnetics, optics, acoustics etc.
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You will deploy and deliver technical solutions at the intersection of computational chemistry and machine learning, supporting research directions in molecular design across broader gRED and Roche.
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machine learning applied mathematics predictive modeling jobs Title: senior machine learning engineer in Anoka, Minnesota
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