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Master’s degree in a quantitative field such as economics, finance, mathematics, data science, or computer science. Contribute to internal written analysis and research-based papers on topics such as quantitative modeling, data and risk analysis, and trends in the banking industry.
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Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
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You have 7+ years of experience working as a professional data scientist/analyst or in a research role (includes PhD or post-doc experience in a quantitative discipline). · You have practical experience with big data technologies such as Spark, AWS, Databricks.
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Bachelor’s degree in math, physical science, data science, analytics or related STEM field. Applies quantitative and qualitative methods to solve complex data and analysis problems in a dynamic environment.
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Bachelor's degree in engineering, Mathematics, Statistics, Computer Science or another quantitative field. Determine the best methods of integrating data from different sources and formats - Oracle Business Intelligence, Exact, Microsoft Dynamics, etc.
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Typically requires 7+ years experience and a Bachelor's degree in any of these fields: mathematics, statistics, computer science, data science/analytics, or similar quantitative fields (master’s degree preferred.
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Prior hands-on experience with quantitative data science in digital farming and/or agronomy domains. As the leader of our Data Science team, you'll shape and execute research strategies and data strategies; build and refine statistical, machine learning and AI models; and drive impactful outcomes that propel our products to new heights.
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A Staff Data Scientist for Market Regulation Technology will support the establishment of data science and machine learning best practices as FINRA moves to machine learning (ML)-based pattern development.
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Evolve our leading replenishment toolkit, to include iterating and advising with key data science partners on machine learning forecasting strategies and agility to react to trends while collaborating with inventory support partners on enhancing parameters such as ordering and fulfillment quantities, target inventory levels, safety stock needs, etc.
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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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Experience working with large-scale data mining and reporting tools (examples: SQL, MS Access, Essbase, Cognos) and other financial systems (examples: Oracle, SAP, Lawson, JD Edwards) The leader will drive with a large-degree of autonomy tactical to Think Big quantitative analysis for Amazon's specialty Last Mile transportation network (AMXL.
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Minimum qualifications: Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field. Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python.
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Minimum Requirements: Master's degree, or foreign equivalent, in Computer Science, Analytics, Engineering, or closely related quantitative discipline. Job Duties: Analyze data in a fast-paced, data-driven environment to apply scientific methods and quantitative approaches to solve real-world analytical problems.
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Bachelor's or Master's degree in a quantitative field such as Mathematics, Statistics, Computer Science, or a related field; Experience with data visualization tools, such as Redash or Power BI.
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Build and maintain strong collaborative relationships with various key partnering functions, e.g. GI Drug Discovery Unit (GI DDU), GI Therapeutic Area Unit (GI² TAU), Clinical Science, Clinical Biomarker, Quantitative Science, and other functions and teams.
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