Senior Manager, Data Science

Walmart • Bentonville, AR

1d

About The Position

What you'll do... Position: Senior Manager, Data Science Job Location: 1 Customer Dr, Mail Stop# 0215, Bentonville, AR, 72716 Duties: Model Assessment and Validation: 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: 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 conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance, and leverages rational arguments. Guide and mentor junior associates on story types, structures, and techniques based on context. Understanding Business Context: Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings. Translate business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact. Serve as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work. Analytical Modeling: select appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets. Select and develop variables and features iteratively based on model responses in collaboration with the business. Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Identify dimensions and designs of experiments and create test and learn frameworks. Interpret data to identify trends to go across future data sets. Create continuous, online model learning along with iterative model enhancements. Develop newer techniques (for example, advanced machine learning algorithms, auto ML) by leveraging the latest trends in machine learning, artificial intelligence to train algorithms to apply models to new data sets. Guide the team on feature engineering, experimentation, and advanced modeling techniques to be used for complex problems with unstructured and multiple data sets (for example, streaming data, raw text data). Model Deployment and Scaling: deploy models to production. Continuously log and track model behavior once it is deployed against the defined metrics. Identify model parameters which may need modifications depending on scale of deployment. Code Development and Testing: write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements. Create test cases to review and validate the proposed solution design. Create proofs of concept. Test the code using the appropriate testing approach. Deploy software to production servers. Contribute code documentation, maintain playbooks, and provide timely progress updates. Tech. Problem Formulation: analyze the business problem within one's discipline and questions assumptions to help the business identify the root cause. Identify and recommend approach to resolve the business problem to create effective technology focused solutions. Set relevant deliverables based on the established success criteria and define key metrics to measure progress and effectiveness of the solution. Quantify business impact. Data Source Identification: understand the priority order of requirements and service level agreements.

Requirements

• Master’s degree or the equivalent in Analytics, Mathematics, Computer Science or a related field plus 3 years of analytics experience or related experience OR Bachelor’s degree or the equivalent in Analytics, Mathematics, Computer Science or a related field plus 5 years of analytics experience or related experience.
• Must have experience with: functional programming in Python / R and JavaScript to build analytical workflows, capturing complex business logic behind metric design and reporting guidelines using Pandas and Numpy libraries; coding in JavaScript to develop web based analytical tools with dynamic user interface; Web/file crawler design and development to extract data from diverse file and databases systems in Python, Alteryx, and C#; designing KPI in Alteryx and Tableau Prep to measure and report business performance; coding SQL queries with procedures and windows functions to extract data from diverse data sources across Teradata, MS SQL Server, and Hadoop; Tableau dashboard development and Self-Service support with Ask Data to monitor and report key metrics; R-Shiny Web app development for automated decision tools; Financial analysis of multiple strategic programs to evaluate the performance across cost and savings using SOLVER; Agile project management with detailed story authoring and documenting in JIRA; Time Series analysis in Python/R to identify the patterns in operational data across diverse time periods; Linear Regression analysis in Python/R to identify relationships between diverse KPIs; designing reports for key business performance monitoring in Excel and PowerPoint; SQL Table design to store real-time data to support reporting and analytics in MS SQL Server and Teradata; and forecasting based on Time-Series analysis of historical data for long range and short-range planning.
• Employer will accept any amount of experience with the required skills.

Responsibilities

• Model Assessment and Validation: 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: 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 conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance, and leverages rational arguments. Guide and mentor junior associates on story types, structures, and techniques based on context.
• Understanding Business Context: Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings. Translate business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact. Serve as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work.
• Analytical Modeling: select appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets. Select and develop variables and features iteratively based on model responses in collaboration with the business. Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Identify dimensions and designs of experiments and create test and learn frameworks. Interpret data to identify trends to go across future data sets. Create continuous, online model learning along with iterative model enhancements. Develop newer techniques (for example, advanced machine learning algorithms, auto ML) by leveraging the latest trends in machine learning, artificial intelligence to train algorithms to apply models to new data sets. Guide the team on feature engineering, experimentation, and advanced modeling techniques to be used for complex problems with unstructured and multiple data sets (for example, streaming data, raw text data).
• Model Deployment and Scaling: deploy models to production. Continuously log and track model behavior once it is deployed against the defined metrics. Identify model parameters which may need modifications depending on scale of deployment.
• Code Development and Testing: write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements. Create test cases to review and validate the proposed solution design. Create proofs of concept. Test the code using the appropriate testing approach. Deploy software to production servers. Contribute code documentation, maintain playbooks, and provide timely progress updates.
• Tech. Problem Formulation: analyze the business problem within one's discipline and questions assumptions to help the business identify the root cause. Identify and recommend approach to resolve the business problem to create effective technology focused solutions. Set relevant deliverables based on the established success criteria and define key metrics to measure progress and effectiveness of the solution. Quantify business impact.
• Data Source Identification: understand the priority order of requirements and service level agreements.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Number of Employees: 1-10 employees