Data Scientist - 2

The Data Scientist - 2 at our company is tasked with developing deep learning models and building a robust recommendation engine for item and product embeddings. You will be working on creating solutions to improve category management, demand forecasting, pricing, promotions, and assortment optimization. You'll also collaborate closely with other team members to ensure the successful implementation of data-driven projects.

To qualify for the Data Scientist - 2 position, you should have at least 10 years of experience in the field. Proficiency in SQL and Python is a must, along with a working knowledge of R as necessary. Experience with PySpark on Databricks using GCP is also essential, particularly in retail contexts dealing with category management and demand forecasting.

The Data Scientist - 2 position significantly impacts retail operations by developing data-driven solutions that improve decision-making processes. By leveraging deep learning and advanced analytics, you will help optimize category management, refine pricing strategies, enhance promotional effectiveness, and improve assortment optimization, ultimately leading to better customer experiences and increased sales.

In the Data Scientist - 2 role, you'll leverage a variety of technologies. Key tools include SQL for database management, Python for data manipulation, and R for statistical analysis, as needed. PySpark will be a central component of your work within the Databricks core development platform on GCP, allowing you to handle large datasets efficiently.

As a Data Scientist - 2, you will engage in diverse projects focused on developing a recommendation engine and deep learning models. These projects typically involve improving retail processes through better demand forecasting, pricing strategies, promotional activities, and assortment optimization, all aimed at maximizing operational efficiency and enhancing customer experiences.

When discussing deep learning in retail, focus on specific projects where you've implemented models to enhance product recommendations or customer insights. Provide examples of algorithms you used and the impact they had on operational performance.

To effectively answer this question, outline your methodology for demand forecasting, including data sources, analytical models you prefer, and any software tools you employ. Highlight a project where your forecasting led to significant improvements.

Share your proficiency in SQL by discussing specific queries, data manipulation, and analysis scenarios you've encountered in past projects. Emphasize your ability to extract meaningful insights from complex datasets.

Discuss a specific experience with PySpark, detailing the project goals and the challenges faced, such as data volume or processing speed. Explain the solutions you implemented and the outcomes you achieved.

Talk about your model validation processes, including cross-validation techniques, performance metrics you focus on, and any tools you use for testing. Demonstrate why accuracy is crucial in the context of retail data science.

Provide a detailed account of a project where data science led to better pricing strategies. Discuss the analytical techniques used and the results, focusing on how it enhanced profit margins or customer acquisition.

Discuss your experience with R, emphasizing how you’ve used it in conjunction with other tools like Python and SQL. Mention specific statistical analyses or visualizations you've created to drive insights.

Describe your strategies for continual learning, such as following leading publications, attending conferences, or engaging in online courses. Highlight how this knowledge informs your work in data science.

Identify specific challenges such as data quality issues, dynamic market conditions, or integrating insights into business strategies. Offer solutions or approaches you've taken to overcome these issues in the past.

Outline your approach to translating complex data insights into actionable recommendations. Discuss tools and techniques you use, such as visuals, storytelling, or structured presentations, to engage non-technical audiences.