Lyft, Inc. is a rapidly expanding ride-sharing company based in San Francisco, operating in 644 cities across the United States and 12 cities in Canada. Founded in 2012 as a part of a long-distance car-pooling business originally called Zimride, Lyft launched in Silicon Valley in 2013. The company has quickly spread, expanding from 60 US cities in April 2014 to over 300 by January 2017. Now, Lyft has grown to over 23 million users, with a billion recorded rides as of 2020.
Lyft generates millions of data points daily and needs to scale out its data science and research science capabilities. Hence, the dedicated data science and business intelligence department is tasked with leveraging the most advanced analytics, machine learning, and big data (using AWS S3/AWS EC2) tools in providing business models and insights.
The data science capabilities at Lyft are split into three specific teams: Data Scientists, Research Scientists, and Machine Learning Engineers.
Data scientists are responsible for building an analytics infrastructure, creating models, and setting up dashboards for self-service analytics. Originally branded as data analysts, the data scientist role at Lyft is much more focused on analytics and being embedded with product managers to drive product decisions forward.
Research scientists at Lyft function more as traditional data scientists and ship production code to work on the core machine learning projects, such as the estimated ride time and the pricing of each ride. Lyft research scientists work a lot on the automation engines that run the Lyft app and product.
Lastly, machine learning engineers at Lyft focus on building the infrastructure needed to host the complicated models that the research scientists build.
Data science roles at Lyft are tailored specifically to teams in different products and features. The precise role and responsibilities will depend on the teams and products/features you are assigned to.
Whatever the teams you are working with or product/features your team is assigned to, the role of a data scientist at Lyft will span across business analytics, modeling, machine learning, and deep learning implementation.
Lyft has a culture of creating an “open, inclusive, and diverse environment where members are recognized for what they bring to the table”. Lyft prefers to hire highly qualified applicants with 3 years plus experience in data analysis and data visualization.
Other basic requirements for hiring at Lyft include:
There is dedicated data analytics and business intelligence department at Lyft, but depending on the teams and product you are assigned to, the job role and function may differ a little. Depending on the teams, the functions may include:
Data science teams at Lyft include:
Lyft’s data science interview process starts with an initial phone screening with a hiring manager or HR, a take-home challenge (with usually 24 hours delivery time), or a technical screen. After successfully passing through both the take-home challenge and a 45-minute long technical interview (two technical interviews in some cases), it is followed by five or six one-on-one interviews on-site, now virtually due to covid-19.
This interview is mainly exploratory and resume-based. The main focus here is on assessing your background, especially past experience, roles, and team dynamics, to determine if you are a potential fit. The initial screening is done via a phone call from an HR or a hiring manager.
See our guide to data science behavioral questions.
The next step in the interview process is the technical interview phone screen with a data scientist. This interview lasts between 30 and 45 minutes. Lyft’s data science interview questions span the fundamentals of probability, statistics, machine learning, business case study, the definition of some operational KPIs, a walk-through of the maths from your hypothesis testing, and your technical/past project experiences.
After completing the initial screening, you will receive a Lyft take-home challenge that you will have 24 hours to complete. Questions in the take-home challenge are case-study-based questions (ridesharing dataset), and they comprise both technical and business side problems. In this challenge, questions typically span across different topics, such as churn rate measurement, optimization (using machine learning), designing/experimentation for recommendations, and creating a comprehensive report about your assumptions, limitations, and conclusions.
Check out the Lyft take-home challenge on Interview Query.
After passing the technical screen, the next scheduled interview in the process is the on-site interview. This process comprises five or six one-on-one rounds of interviews with a data scientist or a team manager, each lasting for approximately 45 minutes. This is a half-day interview process involving whiteboard coding, project discussion with team managers and data scientists, business case studies, and statistical concepts.
In general, the cumulative interview process will look like this:
Note: At this stage, you are expected to build a coherent story around your analysis while answering questions on what metrics you used and why you chose them.
The interview process aims to assess your experience with analytical concepts and design skills in providing business impact insights. Remember to brush up on your knowledge of statistics and probability (A/B testing), experimental design, and the business applications of key statistical concepts.
Also, reading up on key economic metrics and KPIs, algorithms, and models will be helpful. Practice lots of SQL interview questions, as these can better prepare you for the technical aspects of the interview process.
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