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Predictive Modeling, Data Visualization, Dynamic Benchmarking. Title: Health Actuary - Predictive Analytics. Health Actuary - Predictive Analytics. This role will be hands-on with building and implementing predictive models for healthcare organizations with strong technical skills using SAS, SQL, R, Python, Power BI, etc.
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The purpose of the Analyst, Predictive Modeling position is to analyze data to drive better business decisions, including: Pricing and credit risk, loan servicing strategies, business policies, processes, and performance, and champion/challenger tests.
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Assess predictive modeling outputs to strategically forecast quarterly enrollment trends, melt, and yield, and in turn, takes action to achieve goals; offers guidance based on analysis of market trends, from data available from DePaul EMM/IRMA, and external sources.
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The Vice President of Health Analytics will thrive in a fast-paced environment and serve as the expert on healthcare trends, predictive modeling methodologies, benchmarking and interpretation of data, innovative payment methods, program evaluation, quality metrics and analytic modeling.
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Demonstrated experience in applying advanced analytics techniques, including predictive modeling, machine learning, and/or statistical analysis, to solve complex business problems.
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Knowledge of data mining and analytic methods such as Clustering / Cluster Analysis, Factor Analysis, Regression Models, Logistic Regression, Predictive Modeling, optimization models, etc.
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Proficient in data modeling, statistical analysis, and predictive modeling techniques, alongside a strong understanding of data visualization best practices and design principles. Proficiency in data modeling principles and techniques, statistical analysis, and predictive modeling techniques.
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Data science, AI, machine learning, operationalization, predictive modeling, and more. Cyber Threat Modeling and Simulation Engineer, Lead. As an analytics professional, you're excited at the prospect of unlocking the secrets held by a data set, and you're fascinated by the possibilities presented by IoT, machine learning, and artificial intelligence.
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Utilize advanced analytics and data science techniques, such as statistical analysis, machine learning, and predictive modeling, to analyze marketing data and predict future trends.
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Oversee data modeling, predictive analytics, geospatial analytics, and other investigative techniques employed by DART. - Develop and oversee the ongoing data analytics, data mining, data matching, data quality, and data sharing components, using software such as SQL and Oracle, that are used by internal and external partners including OPA, FIA, MICSA, HASA, ACS, and DHS.
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We create open-source solutions that collect, reconcile, transform, and standardize data for business intelligence, predictive analytics, decision support, and user transactions. Ability to uncover data-driven insights using statistical analysis or predictive analytics.
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Leads and facilitates workforce planning initiatives including, but not limited to, critical role/capability identification, environmental scanning, forecasting and predictive modeling, gap analysis, risk analysis, action planning.
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You have 2-5 years of successful technical experience in the domain of predictive analytics (e.g. data science, machine learning, data mining, and statistics related work); preferably in the Oil & Gas industry.
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Develop, implement, and optimize machine learning models to detect money laundering, fraud, and other financial crimes, leveraging techniques such as anomaly detection, clustering, and predictive modeling.
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As a Financial Crime Compliance Technology Analyst, you will play a crucial role in leveraging machine learning, analytics and visualization techniques to enhance our organization's capabilities in detecting and preventing financial crimes.
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