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Continue to develop and help drive the strategy for the organization's data strategy including data architecture, Reporting, Insights, Analytics, Operations, Predictive modeling, attribution and Machine Learning/AI.
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Continuous expansion of knowledge relating to credit scoring, credit bureaus, score models, and auxiliary scores including artificial intelligence (AI) and machine learning models to effectively monitor & analyze trends and model potential changes to score models and thresholds.
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Collaborate with baseball subject matter experts in scouting, development, biomechanics, machine learning, decision science, and more, integrating their expertise into player evaluation models.
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Strong technical and consultative experience managing ESI through the EDRM including staging, processing, culling, email threading, clustering, communication analysis, machine learning, predictive modeling, etc.
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Specialized experience for this position must include: Experience with data science and analytical methods from conducting machine learning, Natural Language Processing, and technical procedures such as, solution design, implementation, and deployment.
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Proving specialization background in data science, including experience with statistical analysis, data mining, predictive modeling, and machine learning; Demonstrating a thorough level of knowledge in statistical modeling, algorithms, data mining, and machine learning algorithms problem solving.
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Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Project Management, Software Development, Stakeholder Relationship Management.
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Uses analytical rigor and statistical methods to analyze large amounts of data, extracting actionable insights using advanced statistical techniques such as data analysis, data mining, optimization tools and machine learning techniques and statistics (e.g., predictive models, lifetime value, propensity models.
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Experience with data modeling, data prep and machine learning tools like Alteryx, RapidMinder, RStudio and Tableau Prep. Experience with statistical and mathematical modeling, artificial intelligence and machine learning software and methods.
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Leveraging machine learning techniques and statistical programming packages to perform analysis on commodity instruments. We assist our clients with analytical support in complex commodity risk topics such as hedging, trading and procurement optimization, market risk management, reporting tools and complex contract modeling, including energy, agriculture, metals, and soft commodities.
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Familiarity with machine learning, model development and related AI techniques, including supervised and unsupervised learning, natural language processing, and predictive modeling.
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Hands-on expertise building, deploying, and managing machine learning models on cloud platforms like Google Cloud (BigQuery, Vertex AI), AWS (SageMaker, Lambda), or Azure (Machine Learning Studio.
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Analyze clinical/genomics data using statistical approaches such as longitudinal analysis, mixed-effect modeling, regression, and machine learning in large EHR-based biobanks, such as the UK Biobank, Million Veteran Program, and Penn Medicine Biobank.
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Progressive design professional with a strong knowledge of computational design technology and emerging applications including: 3D modeling software such as Revit and Rhino; parametric design methodologies using Dynamo and Grasshopper; data and 3D visualization capabilities utilizing PowerBi and Tableau; and multi-variable optimization through Machine Learning & AI.
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Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning.
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modeling machine learning jobs in Harrisburg, PA
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