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Apply machine learning, econometrics/statistics, predictive modeling, return-on-investment analysis, simulation, and data visualization methods to support the development of health policy.
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Expertise in statistical programming, data mining and machine learning tools (Python, SAS, R, etc.) Minimum7 years of experience with SAS or SQL, or other data management, reporting and query tools (SharePoint, SAP, Business Objects, Tableau, Crystal reports/Dashboard, etc.
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Knowledge of machine learning, statistical modeling and optimization, some experience working directly or indirectly in Python or R. Knowledge of foundational analytics toolkit - data extraction, manipulation and visualizations (e.g., SQL, Pandas, Tableau, Ggplot.
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The Senior Staff Data Scientist will contribute to the performance analysis of SoFi products using empirical measurements, develop quantitative and machine learning models to forecast losses and provide insights on the drivers for losses.
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Experience using statistical computer languages (Python, SQL, Scikit-Learn, and well-known machine learning frameworks and techniques) to manipulate data and draw insights from large data sets, and using web services: AWS Redshift, S3, etc.
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Advanced experience designing and generating scheduled and ad hoc reports using reporting/query tools (i.e. Brio, Toad, SQL Developer, MS Access, Crystal Reports, Business Objects, etc.) Report development experience utilizing Microsoft SQL Server Reporting Services (SSRS), Tableau, Business Objects, Crystal Reports or similar report development software.
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3+ years of experience with SAS, Teradata, PySpark, SQL, and Machine Learning (ML) and Natural Language Processing (NLP) & Large Language Model (LLM) experience. 4+ years of data science experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
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Statistical and machine learning techniques for analyzing financial data, including survival analysis, time series analysis, GLM, logistic regression, XGboost, LightGBM, Random Forest, Neural Networks, decision tree analysis, and cluster analyses.
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Knowledge of statistical sampling, optimization, machine learning, predictive modeling, and artificial intelligence techniques. Extracts data using a variety of a programming languages, such as SQL, SAS, Python, or Spark/Scala, and applies knowledge of relational and non-relational database systems housed on both on-premise and off-premise (hybrid) platforms.
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Apply machine learning, data mining, and visualization techniques to make accurate and actionable analyses in a timely fashion. Experience developing and applying statistical or machine learning methods in a corporate environment through applications such as R, python, SAS, SPSS, S, S-PLUS, or Stata.
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As a Principal, you'll design and deliver innovative Machine Learning solutions as part of intelligent products on Amazon Web Services, Azure and Google Cloud using core cloud data science tools, MLOps components, and other big data related technologies.
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Experience with one or more of the following a plus: model development, machine learning / artificial intelligence, and data science. Intermediate to advanced knowledge of one or more of the following: SQL, SAS, Alteryx, Python, Tableau or related tools.
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Experience with machine learning, deep learning, data mining, and/or statistical analysis tools is a plus. Proficiency in Power BI as a data visualization tool required.
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Machine Learning (ML), Natural Language Processing (NLP) and Large Language Model (LLM) experience. Experience with SAS, Teradata, Python, PySpark, SQL. Data Scientist @ onsite (Only w2.
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Statistics Tableau and Power BI PL/SQL and databases both SQL and NoSQL Data structures and algorithms Artificial Intelligence and Machine learning Machine learning Algorithms Decision Tree and Random Forest Algorithms Naïve Bayes and KNN Algorithm Support Vector Machine (SVM) Statistics Random variables, Zscore, Hypothesis testing, Expected Value Predictive Modeling : Different kind of Business problems and different phases of Predictive modeling and Popular Modeling Algorithms.
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