Data Scientist II Interview Experience at Grubhub

I recently went through the interview process for a Data Scientist II position at Grubhub, and it was a comprehensive and challenging experience. Here’s a detailed breakdown of what you can expect based on my experience and other candidates’ insights.

Interview Overview

The Grubhub Data Scientist II interview process is structured but also has a significant focus on problem-solving and real-world application of data science. The company emphasizes the use of machine learning, data analytics, and statistical modeling to improve customer experience and operational efficiency.

Interview Stages

Initial Screening (Phone Interview with Recruiter)

• Duration: 30-45 minutes.
• The recruiter will discuss your background, motivation for applying, and relevant experience. They will also go over your technical skills and the projects listed on your resume. Be prepared to discuss your experience with machine learning techniques, tools like Python, and any SQL or data engineering experience.

Example Question:

• “Tell me about a time you used data science to solve a business problem.”

Technical Phone Screen (1st Round)

• Duration: 45-60 minutes.
• This interview is typically with a Data Scientist from the team. It will test your coding skills (usually in Python) and your ability to work through algorithms and data problems.
  You will be asked to solve problems that involve data manipulation, statistical analysis, and perhaps machine learning model development.

Example Question:

• “Given a dataset, how would you identify anomalies or outliers?”
• “Write a function to calculate the moving average of a time series.”

Some interviews also include SQL questions, like writing complex queries to extract data from a database.

Technical Deep Dive (2nd Round)

• Duration: 1 hour.
• This round focuses heavily on machine learning and data science algorithms. Expect to dive into the modeling process: what techniques you would use, how to tune them, and how to interpret the results. You’ll be expected to explain your thought process and reasoning clearly.

Example Question:

• “How would you approach building a recommendation system for Grubhub’s customers?”
• “How would you assess the performance of a predictive model in a business setting?”

You may also be asked about how to evaluate model performance (e.g., ROC curve, AUC, precision-recall).

Behavioral Interview

• Duration: 30-45 minutes.
• This is a standard behavioral interview where the focus is on teamwork, communication, and problem-solving in the context of past experiences. Grubhub values candidates who can collaborate well with product and engineering teams, as this role involves a lot of cross-functional work.

Example Question:

• “Tell me about a time when you had to work with a team to solve a challenging problem. How did you contribute to the solution?”
• “Describe a situation where you made a data-driven decision that impacted the business.”

Final Round (Onsite or Virtual, Depending on Location)

• Duration: 2-3 hours.
• This is typically a mix of technical challenges and team collaboration assessments. You will likely be asked to present a past project, explaining how you approached the problem, the methodology you used, and the results.

In addition, expect to tackle a case study involving real business problems. You will need to analyze a dataset, make recommendations, and discuss your findings. This is a critical part of the interview as Grubhub values practical insights that can drive business performance.

Example Case Study:

• “Given a dataset containing user interactions, how would you analyze the effectiveness of a new recommendation algorithm?”

You may also be asked to demonstrate your skills in data engineering or tools like PySpark or Hadoop, particularly if you have experience in these areas.

Wrap-Up with Hiring Manager

This is more of a final evaluation where the hiring manager will assess both your technical abilities and how well you fit into the team culture. They’ll ask about your career aspirations and how you align with Grubhub’s mission to improve the food ordering experience through data.

Key Areas to Focus On

• Machine Learning: Be prepared to discuss various models and algorithms (e.g., classification, regression, recommendation systems), along with how to evaluate and deploy them in a real-world setting.
• SQL: You will need to demonstrate strong querying skills, especially for data extraction, transformation, and aggregation.
• Data Engineering: Experience with data pipelines, feature engineering, and tools like PySpark or Hive can set you apart.
• Problem-Solving: Grubhub values candidates who can approach problems creatively and find solutions to optimize user experience and business metrics.

Example Questions

• SQL:
  “Write a query to find the top 5 restaurants based on order frequency in the last 30 days.”

• Python:
  “Given a dataset of delivery times, how would you model and predict the delivery time for a new order?”

• Machine Learning:
  “How would you build a recommendation engine for restaurant suggestions on Grubhub?”

• Statistics:
  “Explain how you would design an A/B test for a new feature, and what metrics you would use to evaluate success.”