TomTom is a leader in navigation and mapping technologies, committed to engineering innovative solutions that enable people to find their way in the world.

As a Data Scientist at TomTom, you will be responsible for designing and prototyping machine learning systems that analyze and optimize large-scale road networks, traffic flow, and parking parameters. This role requires you to develop and implement data structures and algorithms to efficiently manage and access vast amounts of traffic and road data. You will work with advanced data analysis tools, particularly in the Microsoft Azure Databricks environment, and collaborate closely with software engineers to translate prototypes into market-ready products. Mentoring junior data scientists and presenting research findings to both technical and non-technical stakeholders are also key responsibilities.

The ideal candidate will possess a Master's degree in a relevant field such as Computer Science, Mathematics, or Electrical Engineering and have a minimum of two years of experience in data science, particularly within Agile development frameworks. Proficiency in programming languages such as Python, Scala, Java, C++, and SQL, along with experience in working with aerial imagery and 3D point cloud processing, is essential. Familiarity with deploying resources on the Microsoft Azure cloud platform using infrastructure as code is highly valued.

This guide aims to equip you with insights into the specific skills and experiences that TomTom values in a Data Scientist, ensuring you can effectively showcase your qualifications during the interview process.

The interview process for a Data Scientist role at TomTom is structured to assess both technical expertise and cultural fit within the organization. Here’s what you can expect:

1. Initial Screening

The first step in the interview process is a 30-45 minute phone call with a recruiter. This conversation will focus on your background, experience, and motivation for applying to TomTom. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role. Be prepared to discuss your familiarity with machine learning concepts, programming languages, and any relevant projects you have worked on.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment, which may be conducted via a coding platform or through a video call. This assessment will evaluate your proficiency in programming languages such as Python, Scala, or Java, as well as your understanding of algorithms and statistical methods. Expect to solve problems related to data structures, machine learning algorithms, and possibly work with real-world datasets to demonstrate your analytical skills.

3. Onsite Interviews

The onsite interview consists of multiple rounds, usually ranging from three to five interviews with various team members, including data scientists and software engineers. Each interview lasts approximately 45 minutes and covers a mix of technical and behavioral questions. You will be asked to present your past work, particularly any projects involving data analysis, machine learning, or cloud computing on platforms like Microsoft Azure. Additionally, you may be evaluated on your ability to communicate complex technical concepts to both technical and non-technical stakeholders.

4. Final Interview

The final stage of the interview process may involve a discussion with senior management or team leads. This interview focuses on your long-term career goals, your approach to collaboration and mentorship, and how you align with TomTom's mission and values. You may also be asked about your experience with Agile methodologies and how you handle project challenges.

As you prepare for your interviews, consider the specific skills and experiences that will showcase your fit for the Data Scientist role at TomTom. Next, let’s delve into the types of questions you might encounter during the interview process.

TomTom Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a TomTom data scientist interview. The interview will focus on your ability to conceptualize and implement machine learning systems, your understanding of data structures and algorithms, and your proficiency in programming languages and cloud technologies. Be prepared to demonstrate your technical skills, as well as your ability to communicate complex ideas to both technical and non-technical stakeholders.

Machine Learning

1. Can you describe a machine learning project you worked on and the impact it had?

This question aims to assess your practical experience with machine learning and your ability to measure its effectiveness.

How to Answer

Discuss the project’s objectives, the algorithms you used, and the results achieved. Highlight any metrics that demonstrate the project's success.

Example

“I worked on a project to predict traffic congestion using historical GPS data. By implementing a random forest algorithm, we reduced prediction errors by 20%, which helped our team optimize route planning for delivery services.”

2. How do you approach feature selection in a machine learning model?

This question evaluates your understanding of the importance of feature selection in model performance.

How to Answer

Explain your process for identifying relevant features, including techniques like correlation analysis, recursive feature elimination, or using domain knowledge.

Example

“I typically start with exploratory data analysis to identify potential features. I then use techniques like recursive feature elimination to systematically remove less important features, ensuring that the final model is both efficient and interpretable.”

3. What are some common pitfalls in machine learning, and how do you avoid them?

This question tests your awareness of challenges in machine learning projects.

How to Answer

Discuss issues like overfitting, data leakage, and the importance of cross-validation. Provide examples of how you’ve mitigated these risks in past projects.

Example

“One common pitfall is overfitting, which I avoid by using techniques like regularization and cross-validation. In a recent project, I implemented k-fold cross-validation to ensure that my model generalized well to unseen data.”

4. Explain the difference between supervised and unsupervised learning.

This question assesses your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each type of learning.

Example

“Supervised learning involves training a model on labeled data, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior.”

Statistics & Probability

1. How do you handle missing data in a dataset?

This question evaluates your statistical knowledge and data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I often use imputation techniques, such as filling in missing values with the mean or median, depending on the data distribution. In cases where a significant portion of data is missing, I may choose to analyze the impact of missingness on the overall dataset.”

2. Can you explain the concept of p-values and their significance in hypothesis testing?

This question tests your understanding of statistical significance.

How to Answer

Define p-values and explain their role in determining the strength of evidence against the null hypothesis.

Example

“A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A common threshold is 0.05, where a p-value below this suggests strong evidence against the null hypothesis.”

3. What is the Central Limit Theorem, and why is it important?

This question assesses your grasp of fundamental statistical principles.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample data.”

4. Describe a situation where you used statistical analysis to solve a problem.

This question looks for practical application of your statistical knowledge.

How to Answer

Provide a specific example, detailing the problem, the statistical methods used, and the outcome.

Example

“In a project analyzing customer churn, I used logistic regression to identify key factors influencing retention. The analysis revealed that customer engagement metrics were significant predictors, leading to targeted retention strategies that improved our retention rate by 15%.”

Programming and Data Structures

1. What programming languages are you most comfortable with, and how have you used them in your projects?

This question assesses your technical proficiency and experience with relevant programming languages.

How to Answer

Mention the languages you are proficient in and provide examples of how you’ve applied them in your work.

Example

“I am most comfortable with Python and SQL. In my last project, I used Python for data cleaning and analysis, while SQL was essential for querying large datasets from our database.”

2. Can you explain the difference between a list and a tuple in Python?

This question tests your knowledge of data structures in Python.

How to Answer

Define both data structures and highlight their key differences, such as mutability.

Example

“A list is mutable, meaning its contents can be changed, while a tuple is immutable. This distinction is important when deciding which structure to use based on whether I need to modify the data.”

3. How do you optimize a SQL query for better performance?

This question evaluates your understanding of database management and optimization techniques.

How to Answer

Discuss strategies such as indexing, avoiding SELECT *, and using joins efficiently.

Example

“To optimize a SQL query, I often start by ensuring that the necessary indexes are in place. I also avoid using SELECT * and instead specify only the columns I need, which reduces the amount of data processed and speeds up the query.”

4. Describe a time when you had to debug a complex piece of code. What was your approach?

This question assesses your problem-solving skills and debugging techniques.

How to Answer

Explain your systematic approach to debugging, including tools and methods used.

Example

“When debugging a complex data processing script, I used print statements to trace the flow of data and identify where the logic was failing. I also utilized a debugger to step through the code, which helped me pinpoint the issue quickly.”

• Write a query to get the average order value by gender. Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender. Round the answer to two decimal places.

• Write a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Find the index where the sum of the left half equals the right half in a list. Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half. If there is no such index, return -1.

• Write a function sorting to sort a list of strings in ascending order. Given a list of strings, write a function sorting from scratch to sort the list in ascending alphabetical order. Do not use the built-in sorted function. Return the new sorted list.

• Write a query to find the earliest date each user played their third unique song. Given a table of song_plays and a table of users, write a query to extract the earliest date each user played their third unique song. If a user has listened to less than three unique songs, display their name with a NULL date and song name.

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, describe the steps and considerations for building a predictive model to identify which merchants the company should target for acquisition when entering a new market.

• How would you determine customer service quality through the chat box for small businesses on Facebook Marketplace? Your team at Facebook focuses on helping small businesses increase sales through the Marketplace app. Explain how you would assess the quality of customer service interactions via the chat box for small businesses selling items to consumers.

• What business health metrics would you track on a dashboard for an e-commerce D2C sock business? As the person in charge of an e-commerce D2C business selling socks, list and explain the key business health metrics you would monitor on a company dashboard.

• Write a query to determine if user interactions on a website lead to higher purchasing volumes. Given three tables (users, transactions, and events), write a SQL query to analyze whether users who interact on the website (e.g., likes, comments) convert to purchasing at a higher volume than those who do not interact.

• How does random forest generate the forest and why use it over logistic regression? Explain the process of generating a forest in a random forest algorithm and discuss the advantages of using random forest over logistic regression.

• How do we deal with missing square footage data to construct a housing price model? You have 100K sold listings over the past three years for Seattle, but 20% are missing square footage data. Describe methods to handle this missing data to build an accurate housing price prediction model.

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, outline the steps to create a model that predicts which merchants to target for acquisition when entering a new market.

• How do you detect and handle correlation between variables in linear regression? Describe methods to detect and manage correlation between variables in a linear regression model. Explain the consequences of ignoring such correlations.

• How would you design a model to detect potential bombs at a border crossing? Outline the design of a model to detect potential bombs at a border crossing, including the selection of inputs and outputs, accuracy measurement, and testing procedures.

• How many more samples are needed to decrease the margin of error from 3 to 0.3? Given a sample size (n) with a margin of error of 3, calculate the additional samples required to reduce the margin of error to 0.3.

• What is the mean and variance of the distribution of (2X - Y)? Given (X) and (Y) are independent random variables with normal distributions (X \sim \mathcal{N}(3, 4)) and (Y \sim \mathcal{N}(1, 4)), determine the mean and variance of (2X - Y).

• How do you calculate the sample size and power for an AB test? For an AB test with a test group and a control group:

• Explain how to calculate the necessary sample size for accurate measurement.
• If the sample size is sufficient, should the power increase or decrease to measure very small differences between the groups?

If you're excited about using your data science skills to shape the future of navigation and make the world a more connected place, the Data Scientist position at TomTom could be an incredible opportunity for you. At TomTom, you will be at the forefront of developing machine learning systems for large-scale road networks, traffic flow parameters, and more. Beyond a competitive compensation package, you'll have the opportunity to grow professionally with a personal development budget and paid learning days.

If you want more insights about the company, check out our main TomTom Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about TomTom’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every TomTom interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!