Walmart
Senior Manager, Data Science- International
Walmart, Bentonville, Arkansas, United States, 72712
Position Summary:
What you'll do:
Model Assessment and Validation:
Requires knowledge of model fit testing, tuning, and validation techniques (e.g., Chi square, ROC curve, root mean square error etc.); Impact of variables and features on model performance. Identify the model evaluation metrics. Apply best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles.Data Visualization:
Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Multiple story plots and structures (OABCDE); Communication & influencing technique; Emotional intelligence. Generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design appropriate data representation for multiple data sets. Work with User Experience designers and User Interface engineers as required to build front end applications. Present to and influence the team and business audience using the appropriate data visualization frameworks and convey clear messages through business and stakeholder understanding.Understanding Business Context:
Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices. Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings.Analytical Modeling:
Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical methods and best-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks, SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming languages like R/Python; Basic classical optimization techniques (e.g., Newton-Rapson methods, Gradient descent); Numerical methods of optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.). Select appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets.Model Deployment and Scaling:
Requires knowledge of impact of variables and features on model performance; understanding of servers, model formats to store models. Deploy models to production. Continuously log and track model behavior once it is deployed against the defined metrics.Code Development and Testing:
Requires knowledge of coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others. Write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements.Tech. Problem Formulation:
Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. Analyze the business problem within one's discipline and question assumptions to help the business identify the root cause.Data Source Identification:
Requires knowledge of Functional business domain and scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. Understand the priority order of requirements and service level agreements.Data Strategy:
Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability, etc. Drive the execution of multiple business plans and projects by identifying customer and operational needs; developing and communicating business plans and priorities; removing barriers and obstacles that impact performance.
Minimum Qualifications:
Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.
Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in an analytics related field.Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics related field.Option 3: 7 years' experience in an analytics or related field.
Preferred Qualifications:
Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.
Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch), knowledge of accessibility best practices.
Primary Location:
701 S. Walton Blvd., Bentonville, AR 72716-6209, United States of America.
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What you'll do:
Model Assessment and Validation:
Requires knowledge of model fit testing, tuning, and validation techniques (e.g., Chi square, ROC curve, root mean square error etc.); Impact of variables and features on model performance. Identify the model evaluation metrics. Apply best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles.Data Visualization:
Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Multiple story plots and structures (OABCDE); Communication & influencing technique; Emotional intelligence. Generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design appropriate data representation for multiple data sets. Work with User Experience designers and User Interface engineers as required to build front end applications. Present to and influence the team and business audience using the appropriate data visualization frameworks and convey clear messages through business and stakeholder understanding.Understanding Business Context:
Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices. Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings.Analytical Modeling:
Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical methods and best-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks, SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming languages like R/Python; Basic classical optimization techniques (e.g., Newton-Rapson methods, Gradient descent); Numerical methods of optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.). Select appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets.Model Deployment and Scaling:
Requires knowledge of impact of variables and features on model performance; understanding of servers, model formats to store models. Deploy models to production. Continuously log and track model behavior once it is deployed against the defined metrics.Code Development and Testing:
Requires knowledge of coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others. Write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements.Tech. Problem Formulation:
Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. Analyze the business problem within one's discipline and question assumptions to help the business identify the root cause.Data Source Identification:
Requires knowledge of Functional business domain and scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. Understand the priority order of requirements and service level agreements.Data Strategy:
Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability, etc. Drive the execution of multiple business plans and projects by identifying customer and operational needs; developing and communicating business plans and priorities; removing barriers and obstacles that impact performance.
Minimum Qualifications:
Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.
Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in an analytics related field.Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics related field.Option 3: 7 years' experience in an analytics or related field.
Preferred Qualifications:
Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.
Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch), knowledge of accessibility best practices.
Primary Location:
701 S. Walton Blvd., Bentonville, AR 72716-6209, United States of America.
#J-18808-Ljbffr