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What you'll do...

Position: Senior Manager, Data Science

Job Location: 702 SW 8th Street, 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.

Minimum education and experience required: 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.

Skills required: 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.

Wal-Mart is an Equal Opportunity Employer.

Pay Rate...

$177,000.00
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What You Should Know About Senior Manager, Data Science, Walmart

If you're looking to propel your career forward as a Senior Manager, Data Science, then Wal-Mart in Bentonville, AR is the place to be! In this pivotal role, you'll not only assess and validate models but will also push the boundaries of data visualization to represent complex data in an engaging way that speaks to various stakeholders. Collaborating with user experience designers and engineers will allow you to frame your work in practical applications. Your recommendations will guide business decisions, making this role crucial for anyone who wants to get their hands dirty with real-world data challenges. Imagine diving deep into analytical modeling, selecting appropriate techniques for big data, and even deploying those models to improve business performance! With a focus on mentoring junior associates, you'll become a key figure in fostering talent and innovation. A master’s or bachelor’s degree in Analytics or related fields along with your proven experience in programming and analytical methodologies will perfectly position you for this exciting journey at Wal-Mart. Join us and leverage your skills to create meaningful impact in the dynamic world of data science!

Frequently Asked Questions (FAQs) for Senior Manager, Data Science Role at Walmart
What are the primary responsibilities of a Senior Manager, Data Science at Wal-Mart?

As a Senior Manager, Data Science at Wal-Mart, you'll oversee model assessment and validation, ensuring accuracy through best practices. You'll also focus on data visualization and communicate effectively with stakeholders to meet business needs. Furthermore, guiding junior associates, developing business cases, and providing actionable insights will be key components of your role.

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What qualifications are needed to apply for the Senior Manager, Data Science position at Wal-Mart?

Candidates for the Senior Manager, Data Science position at Wal-Mart should have a Master's degree in Analytics, Mathematics, Computer Science, or a related field with three years of analytics experience, or a Bachelor's degree with five years of related experience. Proficiency in programming languages such as Python, R, and JavaScript is crucial.

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How does Wal-Mart support continuous learning for Senior Managers in Data Science?

Wal-Mart encourages continuous learning for its Senior Managers in Data Science through mentorship opportunities, access to training materials, and collaboration with peers. You'll have the chance to stay up-to-date with the latest trends in data science, machine learning, and AI, which will help you innovate and lead effectively.

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What tools and technologies will I use as a Senior Manager, Data Science at Wal-Mart?

In the role of Senior Manager, Data Science at Wal-Mart, you’ll utilize a variety of tools including Python, R, Alteryx, and Tableau for data analysis, modeling, and visualization. You'll also work with SQL databases such as MS SQL Server and Teradata to manage and analyze large data sets.

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What is the expected salary for a Senior Manager, Data Science at Wal-Mart?

The salary for a Senior Manager, Data Science at Wal-Mart is competitive, starting at around $177,000. This compensation reflects the significant expertise and experience required for the role, along with the value you’ll bring to the company.

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Common Interview Questions for Senior Manager, Data Science
Can you describe your experience with model assessment and validation?

When answering this question, focus on specific methodologies you’ve used for model validation. Highlight your knowledge of evaluation metrics and best practices for tuning models. Providing examples of how you've improved model accuracy will help demonstrate your expertise.

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How do you approach data visualization when presenting complex data?

Emphasize the importance of tailoring your data visualizations to your audience. Discuss the tools you prefer to use and how you ensure that your visuals clearly convey the message and insights derived from the data. Real-world examples will strengthen your response.

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What programming languages are you proficient in for data analysis?

Be sure to list your proficiency in programming languages such as Python and R, and mention specific libraries or frameworks you utilize, like Pandas or NumPy. Discuss how your coding skills apply to analytical workflows and problem-solving in data science.

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Can you explain how you would identify business requirements for a data science project?

Illustrate your process for gathering business requirements, which may involve interviews, surveys, or stakeholder meetings. Explain how you translate these requirements into actionable data science tasks and ensure they align with business objectives.

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Describe a time when you mentored a junior associate in analytics.

Use this opportunity to describe your mentoring style and how you tailor your guidance based on the individual's needs. Discuss any outcomes from your mentorship, such as improvements in their performance or successful project completions.

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How do you stay current with analytical methodologies and trends in data science?

Mention specific conferences, online courses, or publications you follow to stay updated on data science trends. Explain how you apply this knowledge to your projects and incorporate new methodologies into your work.

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What experience do you have with deploying models to production?

Detail your experience with model deployment, including the techniques and frameworks you’ve utilized. Discuss how you monitor model performance post-deployment and any adjustments you've made based on performance metrics.

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What is your approach to formulating and analyzing business problems?

Describe your systematic approach to problem formulation, including understanding the business context, asking the right questions, and identifying root causes. Emphasize your analytical skills and creativity in developing effective solutions.

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How do you ensure the accuracy of your analyses?

Discuss your process for validating your analyses, including the cross-verification of data sources, peer reviews, and applying statistical tests for accuracy. Providing specific examples will help illustrate your thoroughness.

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Can you discuss a successful project where you applied advanced machine learning techniques?

Outline a specific project where you employed machine learning algorithms and explain the problem you solved, the data used, and the results achieved. Highlight your role in the project and any particular challenges you overcame.

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