doordash case study interview

Routing, scheduling, optimizing delivery queues for profit in various ways such as segmenting them, getting a better geographical understanding of tips, evaluating and ranking driver performance for performance reviews... You could even propose additional datasets to collect for specific purposes, such as turnover for correlation to delivery performance and tips, to evaluate whether pooling tips in teams or across the board would improve overall performance. Preventing thousands of dollars a day in fraud losses. I am totally blanking on where to prepare for the case study rounds. (2) You receive a take-home challenge where you will be graded on your ability to build a machine learning model. Drivers efficiently tackle hills, gridlock and parking issues, no sweat. What type of scenarios, style of case studies can be expected. Data Science Machine Learning: This team sits right in the middle of the former two. These links curate valuable business context as well as quotes from interviews and news features. We have both at Interview Query. Analytics Data Scientist: This team focus on experimental analysis with emphasis on building dashboards and doing the analysis that supports specific business goals. Additionally after that, any company with marketplace effects. When it comes to having your favorite food delivered, few vehicles can rival the efficiency of an electric bicycle in traffic-congested cities. e-Bikes for Doordash Couriers Across the Country. I am a bot, and this action was performed automatically. Dashers (delivery people) have the freedom and flexibility to work when they want, while restaurants are empowered to reach a greater pool of customers. You can unsubscribe at any time. By Adam Nathan - April 11, 2017 (@adambnathan), DoorDash delivers food on-demand and on time to consumers and B2B. Proficiency in numerical programming languages (Python preferable). You will be introduced to the data scientist team along with other team members that work closely together. At this point, you will be asked a series of questions about the techniques used. EA / Data nvcV82 ... function; I am preparing for upcoming data science interview. If your business model includes the logistical orchestration of people and items, this 5 Minute Big Data Case Study is for you. The manager and the case study interviewer were courteous and well-versed. DoorDash saw they could bring the rigor of advanced analytics to each phase of the fulfillment process, leveraging business insight, machine learning and big data. They're looking for some imagination... Come on, you don't need a PhD to think up some problem statements for a dataset like this. If a signal is flagged as potentially risky, the team has the knowledge to better understand whether that’s typical of good users or if it’s something they should be concerned about. Here's a brief sketch of the business pain they're attacking in both the customer and vendor experience, the critical insights driving their approach, and some major takeaways from their journey. Learn how Sift helps companies grow securely, Want to get in touch? I have a case study interview with doordash. Polish your object-oriented programming skills as you may be asked to modify an existing program with OO techniques. Operating in the US, Canada, and Australia, DoorDash has both a desktop site and a mobile app, with most traffic coming through the iOS app. There are some interesting projects they're working on. Jobs. Most questions asked in the on-site interview are open-ended. DoorDash interview details: 608 interview questions and 522 interview reviews posted anonymously by DoorDash interview candidates. Since implementing Sift, DoorDash is preventing thousands of dollars a day in fraud losses. "We’ve broken down every step of your food’s journey to a perfect science.". These links curate valuable business context as well as quotes from interviews and news features. Continue Reading. Which is why it was such a no-brainer for food delivery service DoorDash to partner with GenZe to launch an e-Bike delivery program in Washington D.C., San Francisco, Los Angeles and Vancouver, British Columbia. I interviewed at DoorDash. They are the closest to the critical insights to drive the analytics, Hyper focus on getting one small piece working first, then scale, Expect to iterate and experience repeated failure. Thanks for taking the time to read this post! GenZe e-Bikes are a cost-effective solution for short-distance deliveries. think of a business problem that doordash probably faces that you can tackle with ML using this data set, build a model, and then offer concrete recommendations to the business based on it. Here are some questions asked previously at the DoorDash Data Science interview. The interviewer is just trying to get a grasp of your thought process and understanding why you made certain decisions. The amount of collective data we have access to with Sift is what makes Sift so valuable to us. they want to see how you can offer actionable insights to the business and use your data science knowledge to do so. Then for these, have some graphs and explanation and package it in a Powerpoint deck, is there something in the data that you can model delivery times or missed deliveries? The manager and the case study interviewer were courteous and well-versed. Product metrics and business case study; Culture fit and behavioral interview; During the on-site interview, you may be given a real-life DoorDash problem to work on and present to the interview panel as various team members pair program with you. The wealth of data that DoorDash has available to them in being part of Sift’s global network has been invaluable to the Risk team for not only identifying fraud but recognizing the behaviors of good users, as well. Report this post; Adam Nathan Follow Director of Professional Services, Data Analytics. a friend of mine received a similar case study and mostly did EDA and data viz stuff with it and put it all together in a PowerPoint deck iirc. Jobs. The user is prevented from returning to the platform, and the machine learning model recognizes that fraudulent behavior in other users. Online businesses using Sift Digital Trust & Safety benefit from the shared knowledge of over 34,000 sites and apps on our platform. I have done some EDA- eg what times of day are the most orders made, do some drivers make more from tips than others Please contact the moderators of this subreddit if you have any questions or concerns. Phone screen (45 min interview with product case), then virtual onsite (5 interviews - mix of behavioral and case). You know their business model, you know what data they have, you're expected to think of some ways to improve their business using the data, then execute. Dashers are faster and more nimble on electric bicycles. Currently interviewing at DoorDash, had 20min interview with manager, passed the take home case study, now two 30min interviews with other managers scheduled tomorrow. See all 34 posts The Mission Press question mark to learn the rest of the keyboard shortcuts, What project(s) have you worked on that demonstrate your skills? Make the internet a safer place — Grow your career. DoorDash is making more informed decisions, thanks to the shared intelligence of the global network, and as a result they’re ensuring their platform is a safe place for Dashers, merchants, and customers. As a result of their growth, they need to grow their data science team to help scale their business. Most interviewers were okay, but one (head of consumer product ) was arrogant and totally turned me off. Machine Learning Engineers: This team focus on building the bulk of the infrastructure for deploying models.

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