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What you'll do...

Position: Data Scientist

Job Location: 702 SW 8th Street, Bentonville, AR 72716

Duties: Demonstrates up-to-date expertise and applies this to the development, execution, and improvement of action plans by providing expert advice and guidance to others in the application of information and best practices; supporting and aligning efforts to meet customer and business needs; and building commitment for perspectives and rationales. Provides and supports the implementation of business solutions by building relationships and partnerships with key stakeholders; identifying business needs; determining and carrying out necessary processes and practices; monitoring progress and results; recognizing and capitalizing on improvement opportunities; and adapting to competing demands, organizational changes, and new responsibilities. Models compliance with company policies and procedures and supports company mission, values, and standards of ethics and integrity by incorporating these into the development and implementation of business plans; using the Open Door Policy; and demonstrating and assisting others with how to apply these in executing business processes and practices. Leads small and participates in large data analytics project teams by serving as a technical lead for analytics projects; working with project teams and business partners to determine project goals; developing contingency plans for data analysis; determining modeling based on business needs; directing the analysis of data; gathering data and developing reports as needed; utilizing business knowledge to ensure data supports project goals; analyzing data based on identified variables; reviewing data results to ensure accuracy; and communicating results and insights to the project team and business partners. Presents data insights and recommendations to key stakeholders by developing insights based on data analysis; applying analytical results to project goals; identifying trends and key insights; translating results into business actions; and presenting insights and recommendations to key stakeholders. Participates in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; building and maintaining a library of reusable algorithms for future use; ensuring developed code is documented; and coaching and mentoring analysts across the division and project teams. Develops analytical models to drive analytics insights by gathering data from internal and external sources; evaluating data usability based on project goals; synthesizing data into large datasets to support project goals; developing statistical models and computational algorithms to analyze data; utilizing the analytics project lifecycle process to drive predictive modeling; coding, testing, and maintaining analytical software tools; identifying trends, patterns and discrepancies in data; training statistical models for replication for future projects; and presenting data insights and recommendations to key stakeholders.

Minimum education and experience required: Master’s degree or the equivalent in Statistics, Analytics or a related field plus 1 years of experience in data science or a related field; OR Bachelor’s degree or the equivalent in Statistics, Analytics or a related field plus 2 years of experience in data science or a related field.

Skills required: Must have experience with: Programming Languages (Python, R Programming, PySpark); Query Languages: SQL; Data Science Techniques: Natural Language Processing, Unsupervised Learning, Supervised Learning, Anomaly Detection, Time series forecasting, Statistical Hypothesis Testing, Data Compression Techniques, Data Munging & Imputation; Visualizations Tools: Tableau, Microsoft Power BI, R Shiny, StreamLit; Cloud Technologies: Google BigQuery, AWS S3, Microsoft Azure Databricks; Unit Testing of code, GitHub code version management; Databases: SQL Server, Teradata, SAP HANA, DBeaver, MySQL, NoSQL, PostgreSQL; ETL Tools: Alteryx; Datawarehouse: SAP HANA, SAP BW; SAP Reporting Tools: SAP BOBJ, Analysis for Office, SAP Lumira, SAP Analytics Cloud, SAP Bex Explorer; and Methodologies: Agile (Scrum and Kanban). Employer will accept any amount of experience with the required skills.

Wal-Mart is an Equal Opportunity Employer.

Pay Rate...

$120,000.00
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Average salary estimate

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$120000K

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What You Should Know About Data Scientist, Walmart

Are you ready to take on the challenge of being a Data Scientist at Wal-Mart in Bentonville, AR? This is an amazing opportunity where you'll dive deep into the data landscape, providing invaluable insights that help shape business strategies! In this role, you'll leverage your expertise to develop and improve action plans while engaging closely with various stakeholders. Your analytical skills will come into play as you lead data analytics project teams, drive project goals, and create innovative solutions. Imagine gathering and synthesizing data from multiple sources, using your experience in Python, SQL, and data visualization tools like Tableau to present trends and patterns that can directly influence decision-making. You'll be responsible for crafting analytical models and developing replicable solutions that enhance future projects. With a focus on continuous improvement, you will also mentor and coach budding analysts, sharing your knowledge of crucial data science techniques such as NLP and supervised learning. If you have a Master's degree in Statistics, Analytics, or related fields and experience with advanced data techniques, we want you! Join us at Wal-Mart, where every insight counts in making a difference!

Frequently Asked Questions (FAQs) for Data Scientist Role at Walmart
What are the responsibilities of a Data Scientist at Wal-Mart?

As a Data Scientist at Wal-Mart in Bentonville, AR, your responsibilities will encompass developing and executing action plans, building relationships with stakeholders, and leading data analytics project teams. You'll be involved in gathering and analyzing data, applying complex analytical techniques, and developing insights that drive business decisions. Also, you will coach junior analysts, ensuring a standard of excellence in the execution of data-driven projects.

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What qualifications are needed for the Data Scientist position at Wal-Mart?

To be considered for the Data Scientist role at Wal-Mart, you must hold a Master's degree or the equivalent in Statistics, Analytics, or a related field, along with at least one year of data science experience. Alternatively, a Bachelor's degree in the same or related field and two years of experience in data science will also qualify you. Proficiency in programming languages like Python and SQL and familiarity with data visualization tools are crucial.

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What technical skills should a Data Scientist have at Wal-Mart?

A Data Scientist at Wal-Mart should possess strong technical skills including proficiency in programming languages such as Python and R, as well as expertise in SQL for data querying. Familiarity with data visualization tools like Tableau and Microsoft Power BI is essential. Knowledge of cloud technologies such as Google BigQuery and AWS, coupled with experience in machine learning techniques (e.g., NLP and anomaly detection), will set you apart.

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What is the team culture like for a Data Scientist at Wal-Mart?

Wal-Mart promotes a collaborative and inventive environment for Data Scientists. You'll work closely with diverse project teams, engaging with peers to harness diverse perspectives and foster innovative solutions. The culture emphasizes continuous improvement, mentorship, and open communication, making it a fulfilling place for anyone eager to share their knowledge and learn from others.

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What can I expect in terms of career development as a Data Scientist at Wal-Mart?

As a Data Scientist at Wal-Mart, you'll have ample opportunities for career development. The company encourages continuous learning and improvement, offering mentorship programs where you can hone your skills. Engaging in various projects will expose you to innovative practices and emerging technologies, all while contributing to your professional growth within a major industry leader.

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Common Interview Questions for Data Scientist
Can you explain your experience with statistical modeling?

When answering this question, detail the statistical models you've worked with, emphasizing how you utilized them to derive actionable insights. Discuss any relevant projects where statistical modeling significantly impacted the outcomes, showcasing your analytical thought process.

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How do you approach data cleaning and preparation?

Highlight your experience with data munging and imputation techniques. Talk about specific tools you’ve used, like Alteryx or Python libraries, and explain your methodology for ensuring data quality before analysis.

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What data visualization tools do you prefer, and why?

Discuss your preferred tools such as Tableau or R Shiny, emphasizing their strength in conveying complex data insights. Provide examples when you've successfully used these tools to illustrate trends to stakeholders.

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Can you describe a successful data project you've led?

Narrate a project where you played a pivotal role. Include your objectives, methodology, challenges faced, and the impact of your data insights on decision-making processes within the organization.

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How do you ensure compliance with data governance policies?

Explain your understanding of data governance and compliance protocols. Discuss any frameworks or standards you've adhered to and describe how you integrate these into your data projects.

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What machine learning techniques are you familiar with?

Detail your experience with various techniques, like supervised and unsupervised learning. Provide examples of how you've applied these methodologies in projects, emphasizing tangible results.

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How do you handle competing data priorities?

Discuss your organizational skills and how you prioritize tasks. Provide examples that illustrate your ability to manage multiple stakeholders and project deadlines effectively.

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Can you explain the concept of anomaly detection?

Provide a clear definition of anomaly detection and discuss its significance in data analysis. Mention specific instances where you've implemented this technique and the insights gleaned from it.

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What role does collaboration play in your work as a Data Scientist?

Emphasize the importance of teamwork in your roles. Share experiences where you collaborated with cross-functional teams to align data goals with organizational objectives.

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How do you stay current with advancements in data science?

Discuss your commitment to continuous learning through various channels such as online courses, webinars, professional groups, and industry-related events. Explain how this knowledge enhances your work.

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April 20, 2025

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