Data Scientist III

As a Data Scientist III at Meijer, you will be responsible for delivering innovative data-driven solutions that enhance customer loyalty and digital marketing strategies. You’ll collaborate with various teams to define project requirements and build end-to-end data science pipelines, ensuring alignment with customer experience needs. Additionally, you will proactively identify strategic data-driven solutions, develop business cases, and maintain communication with data consumers to ensure they leverage our data science products effectively.

To qualify for the Data Scientist III role at Meijer, candidates should possess an advanced degree (MA/MS or Ph.D.) in Mathematics, Statistics, Economics, or a related field, along with at least six years of relevant data science experience, preferably within the retail sector. Hands-on expertise in tools such as Python, Azure ML, and experience with large datasets and machine learning systems is essential. Additionally, certifications, such as Azure Data Science Associate and Azure AI, are preferred.

The Data Scientist III role at Meijer is hybrid, requiring candidates to work on-site in the Grand Rapids corporate office three days a week, from Monday to Wednesday. This structure allows for effective teamwork and collaboration while also providing flexibility for remote work.

As a Data Scientist III at Meijer, you will engage with a variety of advanced technologies and programming languages. Essential tools include Python, Azure ML, Databricks, SQL, and PySpark, along with expertise in machine learning frameworks like TensorFlow and Scikit Learn. Experience with Azure Cloud technologies, including Azure DevOps and MLOps, will also be critical as you build and maintain data science systems.

The work of a Data Scientist III at Meijer significantly impacts customer experience by driving customer loyalty, optimizing marketing strategies, and enhancing operational performance. By developing innovative data-driven solutions, you will align with business goals and help create a seamless shopping experience that positively affects both company performance and community engagement.

When answering this question, describe specific machine learning algorithms you've implemented in previous roles, such as supervised or unsupervised learning models. Discuss the context in which you used these algorithms and the outcomes achieved, demonstrating your understanding and hands-on experience in applying machine learning techniques.

To effectively respond, outline your methodology for developing data pipelines, including initial assessments of data sources, defining requirements, skillset, and collaboration with different teams. It's important to emphasize your experience with tools like Azure ML and Python while mentioning best practices in documentation and deployment.

Answer this by detailing a specific project that presented significant challenges, focusing on the problem, the steps taken to address it, and the final results. Highlight your problem-solving skills, collaboration with team members, and how you leveraged data-driven insights to make informed decisions.

In your response, discuss key performance indicators (KPIs) that you have established in previous roles to evaluate the impact of your data initiatives. Highlight how you analyze data product adoption and value, demonstrating not just qualitative insights but also statistical reasoning.

Mention specific tools you are familiar with such as Tableau, Power BI, or Matplotlib for Python. Explain your criteria for selecting a tool based on the project requirements, user friendliness, and how effectively it can communicate data stories to stakeholders.

Discuss your experience with cloud platforms, particularly Azure, highlighting specific services you've used like Azure DevOps and Azure Synapse. Emphasize how cloud technologies enhanced your data project capabilities and your familiarity with MLOps practices.

Express how you collaborate with cross-functional teams, understanding their needs and aligning data science deliverables with strategic objectives. Highlight your experience in developing business cases that connect data insights with tangible business outcomes.

Provide an example where you had to present complex data findings to non-technical stakeholders, discussing how you simplified the language and visualizations. Your response should showcase your ability to bridge the gap between data science and business understanding.

Discuss any previous projects that involved NLP, describing the tasks such as sentiment analysis or text classification. Mention tools and frameworks you used, like NLTK or TensorFlow, and the business implications of your work.

Share your strategies for continuous learning, whether through attending industry conferences, taking online courses, or engaging in data science communities. Mention specific resources you find invaluable in staying updated.