As Data Scientists at Lyft, we are constantly looking for new opportunities to transform the future of transportation. We challenge convention, take risks, and drive impact. Being an intern is a great way to experience what it’s like to be a Data Scientist in this mission and data driven company. During the 12-week internship, you’ll be fully integrated into a Data Science team and paired with a mentor and manager to solve a real-world problem with great impact.

In this post, we’ll be providing best practices and study tips to better prepare internship applicants for the Data Science interview process at Lyft. We aim to demystify the process, share resources with students who are new to Data Science interviews, and make the interview process more inclusive and transparent for students from all backgrounds.

Before learning more about what to expect in the application process, we recommend that all students evaluate their personal interests and background to decide which role to apply for.

Which Data Science Internship Should You Apply For?

The Lyft Data Science team consists of professionals with a variety of backgrounds, interests, and expertise. The team is organized based on the typical output they produce: Decisions or Algorithms.

• Data Scientist, Decisions: “Data Science for Humans”. Decisions Data Scientists influence decisions made by executives, product managers, engineers, operations teams, and other stakeholders. They utilize a deep understanding of the business to develop decision frameworks that drive alignment on the most impactful problems and solutions.
• Data Scientist, Algorithms: “Data Science for Machines”. Algorithms Data Scientists develop models that power internal and external production systems. They typically apply a combination of optimization, machine learning, and other methods to design, improve, and monitor models and systems.

To learn more about the daily responsibilities of Data Scientists across different focus areas in the company, check out our previous article by Simran and Thibault.

Specifically for the internship, students should also decide which track to apply for — Product (DS Decisions), Inference (DS Decisions), Optimization (DS Algorithms), or Machine Learning (DS Algorithms).

The General Interview Structure

Regardless of which type of Data Science role you are looking to apply for, the typical interview process is similar. Resumés will initially be evaluated by recruiters to gauge if the candidate is a good fit for the role to move forward. Generally, you’ll want to make sure that the projects and activities you list on your resumé align closely with the requirements of the job description — make sure to review the job description thoroughly and ensure that yours mirrors the responsibilities and requirements for the job. The key takeaway for resumés is that your experiences should be relevant to the role you’re applying for. For example, if you’re interested in a Decisions-Product track internship, your resumé should ideally include some experience working with large data sets, exploration and deep dives into data, experimentation, proficiency with SQL, and examples of communication skills, among other things. Once your resumé passes the recruiter’s screening, there are three rounds of interviews to get through. In each round you’ll be speaking with a Data Scientist or Data Science Manager.

Initial Interview: Technical Screen

The technical screen aims to test your abilities in several core skills needed for any Data Scientist at Lyft such as communication, ownership, and collaboration. The initial part of the interview will be focused on your background — be prepared to introduce yourself, and talk about your past professional experiences or projects. Remember to put an emphasis on the past experiences related to the track you are applying for. For each project on your resumé, be prepared to provide background, discuss challenges you faced, and describe how you used data to overcome them and improve target metrics.

For the Decisions-Product track, this initial interview also tests your understanding of Lyft’s business, your ability to solve problems relevant to Lyft, and basic statistics & probabilities. Rather than coding, this interview will focus on walking through a problem breakdown and metric selection in the Lyft marketplace. To prepare, we highly recommend learning more about how Lyft works as a transportation company (check out the Lyft Data Science Blog as a starting point!), enhancing your analytical skills and creative thinking, studying basic statistics & probability concepts, and gathering basic knowledge about experimentation (e.g. A/B testing).

For all Algorithms tracks and Decisions-Inference, in addition to basic statistics and probability concepts, the initial interview will also focus on evaluating your domain knowledge and common applications of Inference, Optimization, or Machine Learning, typically through an open-ended business problem at Lyft (but again with no or minimal coding, just discussing a problem with the interviewer).

Homework Assignment

If the Technical Screen goes well, we’ll invite you to work on a take-home homework assignment. You will receive a problem prompt along with a set of data. You will be asked to solve a problem by applying your coding skills and creating a report or presentation (e.g. Google slides) on your key findings. This mini-project will be based on a sample of anonymized Lyft data and is very similar to what you’ll be working on as an intern (but of a smaller scope). This is a difficult part of our interview process, testing a wide range of Data Science skills and can take 6–8 hours. You will have a time window of a few days to complete the assignment, which you will have the flexibility to pick.

Pro tips to develop a good homework assignment solution:

• Perform a comprehensive exploration of the data and clearly state your assumptions.
• Break down the problem and communicate the analysis process in a structured manner in the presentation or report.
• Apply the appropriate technical methodologies while comparing with alternative approaches or techniques.
• Uncover insights and propose strong and clear recommendations that are aligned with Lyft’s values and business goals.
• Consider the varieties of impacts your recommendations would have on the entire Lyft marketplace.

Final Round

The final round for all Data Scientists consists of multiple interviews in one day. We will schedule the final round at a time that’s most convenient for you, to give you ample time to prepare and study.

Coding Interview (All Tracks)

For any Data Science track, the final round includes a coding interview. For the Decisions-Product track, SQL is the default language we’ll test, using an interactive coding environment like Coderpad. In this interview, you’ll be fetching and processing data to answer questions relevant to Lyft, and we recommend brushing up on your SQL skills.

For the Algorithms tracks and Decisions-Inference, the default coding language is Python. This interview assesses your algorithmic and data structures knowledge, as an Algorithms intern will be doing more algorithmic coding as part of your day to day responsibilities. This coding interview is similar to the type of Leetcode and Hackerrank coding questions that is typical during software engineering interviews.

In both cases, if you are interested in using other languages, feel free to let your recruiter know in advance and we’ll try our best to accommodate accordingly.

Experience Interview (All Tracks)

The Value Fit or Experience interview is a common part of the process for any track as well. Similar to the initial Decisions-Product interview, this experience interview will be focused on your background. Be prepared to talk about your past working and project experiences in more depth. We are looking for a candidate who:

• Owns their work end-to-end and takes responsibility for outcomes.
• Takes a rigorous and analytically-driven approach while still being grounded and pragmatic.
• Is empathetic, understands the needs and concerns of others, and acts with integrity to do the right thing.
• Works collaboratively with others and is a team player (see the Lyft guide to making it happen).

Additional Interviews

Additionally, for the Decisions-Product track, prepare to present your homework in a 10-minute presentation, answering questions to clarify alternative approaches, motivations, and the details of your methodologies. It’s hard to predict what questions will be asked during the presentation, as they vary a lot based on the details provided in the presentation. Bottom line: be confident and open to discussion. This interview is your opportunity to showcase your presentation skills and ability to share data insights clearly to an audience — something that Decisions Data Scientists do every day!

For the Inference/Optimization/Machine Learning tracks, there will be a business case instead of a homework presentation. Very similar to the Technical Screen in the first round of the Decisions-Product track described earlier, we aim to test your business acumen, problem solving capabilities, and basic statistics and probability knowledge. Be sure to explain your solutions in a structured manner, identify the most relevant metrics, and understand the potential business trade-offs.

General Tips

Communicate effectively

Communication is critical in any Data Scientist’s day-to-day work, as we work very closely with cross-functional partners across Engineering, Product, Design, Marketing, and Operations as well as other Data Scientists. It is critical for you to demonstrate the ability to communicate business and technical content to a range of stakeholders effectively and accurately during the interview process.

Be open to discussion

Lyft’s interview questions are based on real life problems that Data Scientists tackle. As it’s common that candidates don’t get to the correct answer right away, remember to follow the interviewer’s prompts as they guide you in the right direction.

During the interview process, if you have any questions, feel free to let your recruiter know. We are doing our best to ensure a great experience for you!

Please note that our interview process is subject to change, so candidates may receive interviews that slightly differ from the ones described in this article.

Are You as Excited as We Are?

Lyft Data Science is hiring! We’re always looking for great additions to our growing Data Science team. Join us!