Descripción
Develop new mathematical and statistical models and concepts, including multivariate regression, hierarchical Bayes, random forests, decision trees, and nonparametric statistics, to solve critical and strategic business problems relative to automotive product quality, design, engineering, Service, marketing, sales, and other forecasting. Determine type, structure, and source internal and external data needed to apply developed statistical model and concepts to answer critical and strategic business questions efficiently and accurately. Analyze, cleanse, and organize data pulled from internal Hadoop and external cloud environments using techniques including fuzzy matching and data profiling techniques. Apply knowledge of the business problem and other technical details to impute and build a comprehensive Analytical Data Set. Research current industry technical solutions that have been developed to address similar problems to benchmark mathematical model development options under consideration. Compile and apply mathematical theories and techniques using computer-driven mathematical analysis tools, including mathematica mathematical symbolic computation, python numerical and statistical software packages, R statistical computing tool and packages, and others to solve practical problems in automotive processes such as Product Quality, Design, Engineering, service, marketing, sales. Design experiments to develop and implement analytic and statistical prediction models in controlled and confined markets; analyze outcome of experiments using developed and standard statistical analyses. Design surveys and opinions clinics and use existing surveys and opinion polls as data sources for statistical models to address strategic business goals and imperatives. Create coherent conclusions and business insights from the models’ results of analyses and suggest actionable measures. Develop data analyses to support and improve business decisions based on statistically sound rulings using analytical methods and model averaging techniques. Draw conclusions and make predications based on mathematical analysis of complex, voluminous empirical data to drive effective business best practices and solutions. Conduct technical knowledge transfer to IT operations team members and support operations team to implement in production mathematical models to support daily business decsions.
[Additional Description]
REQUIREMENTS:
Bachelor’s degree in Statistics, Mathematics, Data Science or related field of study. Four (4) years of experience as a Senior Data Scientist, Data Scientist, Statistician or related occupation. Four (4) years of experience with: Data Science: Advanced Analytics techniques including artificial intelligence techniques including various types of neural nets; Machine Learning and Statistical methods including regression, probability analysis, risk analysis, and statistical process control; Data gathering, inspecting, cleansing, transforming, mapping, and modeling diagramming techniques; Databases: Databricks, Azure, Oracle, SQL Server MS Access; and Data integration: SQL, NoSQL, Hadoop, Cassandra, Alteryx, and Trifacta. One (1) year of experience with: Business Intelligence and Reporting Tools: Tableau, Power BI, Excel, JMP, SAS, SPSS, and Cognos.
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Información sobre diversidad
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Declaración de igualdad de oportunidades en el empleo (EE.UU.)
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Adecuaciones (EE.UU. y Canadá)
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