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Basic Qualifications:Bachelor's degreeMinimum of 4+ years of experience in Risk Management, Product Management for Credit Card or Debit CardPreferred Qualifications:Hands-on knowledge of Fiserv platforms and systemsMinimum of 4+ years of experience using data manipulation tools such as SAS, SQL, R, or Python, Business Objects, or other tools to query large databases and manipulate large data files.
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Reasonable familiarity with some statistical tools (SAS, R, Python) and data analytic/visualization tools such as SQL, Tableau, Excel and PowerPoint. May need to participate in the process of ACL Production and/or Stress Testing from input data quality check, model simulation, attribution and gap analyses, result aggregation, deck preparation to final documentation.
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Minimum of 4+ years of experience using data manipulation tools such as SAS, SQL, R, or Python, Business Objects, or other tools to query large databases and manipulate large data files.
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Advanced working knowledge of one or more of the following quantitative programming, data analysis, database skills, numerical and statistical tools such as: Stata, VBA, R, SAS, Python, SQL, and/or MATLAB strongly preferred.
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Experience with analytical or data manipulation tools (e.g. SAS, SQL, R, C Programming in UNIX) is preferred. Ability to challenge models and identify model risk, especially on conceptual soundness, data quality, and model development process.
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Python, R, SQL, Java, SAS, and MATLAB. Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets.
$118,000 - $178,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Strong programming skills that include experience with Tableau, Python, R, Teradata, SAS/ SQL and related analytical experience. Investigate industry data to check loan performance through data analysis, data mapping and data migration.
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Proficiency in data manipulation and analysis via SQL and Excel or a programming language such as Python, R, or SAS. Bachelors Degree in a quantitative discipline such as Quantitative Finance, Statistics, Economics, Mathematics, Data Science, or Actuarial Sciences.
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Demonstrated experience with: Data analytics tools and software such as SQL, Python, R, SAS, or Tableau which is crucial for extracting insights from large datasets. Experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform Evaluating, summarizing, organizing, and interpreting data.
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Experience with languages and tools such as Python, SQL, SAS, Qlik, Tableau, etc. Knowledge or experience working with risk, finance, and or ESG data. Previous experience in banking, with specific emphasis on reporting, business intelligence, systems, technology, data, risk, compliance or related areas4.
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You must be comfortable managing large population datasets in SQL/SAS and have familiarity with healthcare data from medical charts, to claims to medical charts to supplemental submissions for ACA Risk adjustment.
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Knowledge of data warehousing concepts and database management systemsProficiency in programming languages commonly used in data analytics and machine learning. Your Educational and Professional QualificationsBachelor’s degree or Master’s Degree in a relevant discipline 5+ years working in analytics, data science, either in professional services or the financial services industry.
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Interest in working with data, interpreting results, analytic best practices and experience with data analytics tools and data visualization; Proficiency in Microsoft Excel, Tableau, Microsoft Access, SQL, Power BI, Python, SAS, Hive and/or Spark.
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Applies quantitative and qualitative data analysis methods including SAS programming, Structured Query Language (SQL) to extract, transform and analyze data and Visual Basic programming language.
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Prior experience training/mentoring new team members on internal and external data sources, risk policies and procedures and various analytical tools such as Python, SAS, Excel, and data mining.
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