Senior Data Scientist Interview Experience at Grubhub

I recently interviewed for the Senior Data Scientist position at Grubhub, and the process was comprehensive, blending technical challenges, problem-solving, and behavioral assessments. Below is a detailed breakdown of the interview process, what to expect at each stage, and tips for preparation based on my personal experience.

Interview Process Overview

The interview process for the Senior Data Scientist role at Grubhub was structured across multiple rounds, focusing on assessing both technical skills and business acumen. Grubhub is looking for candidates who not only excel in technical analysis but can also translate data insights into actionable strategies to improve business outcomes.

1. Initial Screening (Phone Interview with Recruiter)

Duration: 30-45 minutes.
The first step was a phone screening with a recruiter. This lasted about 30-45 minutes and focused on getting a sense of my background, motivations, and fit for the role.

Key Topics Discussed:

• Motivation for Applying:
  “Why are you interested in the Senior Data Scientist role at Grubhub?”
• Technical Skills:
  “What data science tools and technologies are you most comfortable with (e.g., Python, R, SQL, Hadoop)?”
• Previous Experience:
  “Can you talk about a data science project you’ve worked on that you think aligns well with Grubhub’s work?”
• Availability and Fit:
  “What is your availability like? Are you comfortable with the responsibilities and the team structure at Grubhub?”

The recruiter also gave a brief overview of the role’s expectations, highlighting the need for strong machine learning skills, experience with large datasets, and the ability to collaborate with cross-functional teams (product, engineering, marketing).

2. Technical Phone Screen (1st Round)

The next stage was a technical phone screen with a member of the data science team. This interview focused primarily on coding, statistics, and problem-solving.

Key Areas Covered:

• Coding & Algorithms:
  I was asked to solve a couple of data manipulation problems in Python or R.

  Example Question:
  “Write a Python function to compute the moving average of a time series. How would you optimize it for large datasets?”

  The interviewer wanted to assess my coding ability and how I structure solutions for scalability.

• Statistics & Machine Learning:
  The interviewer delved into statistical techniques used in data science. They asked about different sampling methods, hypothesis testing, and the trade-offs in machine learning models.

  Example Question:
  “How would you select features for a regression model, and how would you evaluate the model’s performance?”

  This was a chance for me to showcase my understanding of model evaluation metrics, such as R-squared, RMSE, and cross-validation.

• SQL:
  Since Grubhub deals with large volumes of data, I was asked a couple of SQL questions to test my ability to extract and manipulate data from relational databases.

  Example Question:
  “Given a dataset of restaurant orders, write a SQL query to find the top 5 most popular items in the last 30 days.”

  The focus was on complex joins, aggregations, and performance optimization (e.g., using indexes).

3. Machine Learning Case Study (2nd Round)

In the second round, I was given a case study related to machine learning model development. The goal of the case study was to test my problem-solving abilities, modeling approach, and communication skills.

Case Study Scenario: The interviewer asked me to design a recommendation system for Grubhub’s platform. The system needed to recommend restaurants to users based on historical order data. I was asked to consider the following:

• Data sources: What features would I use (user behavior, restaurant type, location)?
• Model choice: What type of model would I select (collaborative filtering, content-based, hybrid)?
• Evaluation: How would I evaluate the recommendation system’s effectiveness (e.g., precision, recall, AUC)?

I had to explain my approach step by step, covering:

• Data Preprocessing: How I would clean and prepare the data, handling missing values, normalizing, and feature engineering.
• Model Selection: I explained why I would choose certain models based on the data and the business problem.
• Evaluation and Optimization: I mentioned how I would iterate on the model using grid search for hyperparameter tuning, cross-validation for robust performance evaluation, and online A/B testing for deployment.

This exercise required me to demonstrate my analytical thinking, the ability to design scalable systems, and my understanding of business metrics that drive product success.

4. Technical Deep Dive (3rd Round)

The third round was a technical deep dive with a senior data scientist. This round focused more on discussing my past projects, the challenges I faced, and how I applied data science to real business problems.

Key Topics Covered:

• Past Data Science Projects:
  “Can you walk me through a data science project you’ve worked on that involved a large, complex dataset?”

  I talked about a customer segmentation project I worked on in a previous role, explaining how I used unsupervised learning (e.g., K-means clustering) to segment customers based on purchase behavior, and how those segments were used to target marketing campaigns.

• Advanced Machine Learning Techniques:
  “How do you handle overfitting in your models, and what techniques do you use to avoid it?”

  I discussed techniques like regularization (L1, L2), cross-validation, and ensemble methods (e.g., Random Forests, XGBoost) to prevent overfitting and improve model generalization.

• Data Wrangling & Scalability:
  “How do you ensure that your data processing pipeline can scale to handle large volumes of data?”

  I explained how I’ve used tools like Apache Spark for distributed processing and how I’ve optimized code to reduce computational complexity when working with large datasets.

The interviewer also asked about model interpretability, asking me how I would ensure the business stakeholders could understand and trust the models. I mentioned techniques like SHAP values and LIME for model explainability.

5. Behavioral Interview (Final Round)

The final round was a behavioral interview with the hiring manager and a couple of team leads. This focused on my leadership skills, teamwork, and cultural fit within Grubhub’s environment.

Example Questions:

• Leadership & Mentoring:
  “Describe a time when you mentored a junior data scientist. How did you approach coaching, and what was the outcome?”

  I shared an example where I mentored a colleague on feature engineering for a recommendation system. I emphasized the importance of explaining complex concepts in simple terms and how I encouraged them to take ownership of a part of the project.

• Collaboration with Cross-Functional Teams:
  “How do you ensure that your data science work aligns with the business goals? How do you collaborate with non-technical teams (e.g., product, marketing)?”

  I explained how I regularly met with product managers to align on KPIs and ensure that the models I developed were solving real business problems. I also discussed how I presented results in a non-technical way to ensure the broader team understood the impact.

• Problem Solving Under Pressure:
  “Tell me about a time when you faced a challenging deadline for a data science project. How did you manage it?”

  I discussed a time when I had to deliver insights for an executive meeting with a tight deadline. I prioritized key analysis, streamlined data processing, and communicated effectively with my team to meet the deadline.

Key Skills Grubhub is Looking For:

• Advanced Machine Learning: Expertise in supervised and unsupervised learning, natural language processing, and recommendation systems.
• Data Engineering: Experience in working with large datasets, data pipelines, and tools like Apache Spark, Hadoop, and SQL.
• Problem-Solving: Ability to translate complex business problems into data science solutions.
• Collaboration: Strong teamwork and communication skills to work with non-technical stakeholders.