The data science machine learning take-home challenge is also two parts. /*# sourceMappingURL=https://www.redditstatic.com/desktop2x/chunkCSS/IdCard.8fe90067a922ef36d4b6.css.map*/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. Plus, the problems we’re solving here affect the real world in a very tangible way. For me, it’s ideal.

That’s why strong communication skills are critical, along with a deep understanding of how our work impacts business fundamentals and how changes can be operationalized. For as long as most data analysis and scientists without PhDs fail to deliver this minimum, we're going to keep being pressured to go for that PhD to further our career, just based on stereotype. Rohan: I’ve been at DoorDash for about two years now. Practice system design using real projects. Their Commitment to Diversity and Inclusion. The coolest part about it is that it translates directly to our business metrics. There are people who use the infrastructure everyday as their their full-time jobs. ( Log Out /  This is a burger and shake. I started out as a General Manager, helping to launch two of our early markets.

You’ve definitely touched on this some, but why did you say yes to working here in the first place? For future deliveries, we use that information to predict each component and set the Dasher up for success, and also set expectations for the consumer.

See who DoorDash has hired for this role . This is one example of the data infrastructure work we do. What type of scenarios, style of case studies can be expected. This initial phone call interview by a recruiter usually last for 30 minutes. Obviously, diversity is really important to me. What project(s) have you worked on that demonstrate your skills? The first part requires building a model to predict delivery duration while the second part is to create an application that can serve the model from part 1. Please contact the moderators of this subreddit if you have any questions or concerns. All Rights Reserved, Data Scientist, Analytics (Multiple Levels), Senior Data Scientist, Analytics (Multiple Teams). The areas of particular interest for me were also business priorities. I transitioned to tech after founding a social gifting app while getting an MBA from the University of Pennsylvania Wharton School. At the interview stage you don't even have to do many of these things, you just have to have ideas on how to improve the business, and ideas on how to execute them. Let’s say I’m a data scientist and I’m interested in DoorDash, what would you want me to know about your team? “We are helping everything go as smoothly as possible, from the time an order is placed to its arrival at your doorstep.”. I’m working on the dispatch team right now, which boils down to the execution of deliveries. Practice data science interview questions from top tech companies delivered right to your inbox each weekday, 26 Oct 2020 – But I gravitated towards problem-solving, asking questions like “What’s going wrong and how do we fix it?” “What’s going well and how to do we double down?” We didn’t have a BizOps team or an analytics team at the time, and Tony, our CEO, suggested I move to California and be a full-time problem-solver. EA / Data. DoorDash is a technology company that connects customers with their favorite local and national businesses in over 4,000 cities and all 50 states across the United States and Canada. We’re kind of the hub. This requires massive amounts of research and problem solving with real-world data. Are you the main person on the engineering team who’s working with data, or are there several people? Someone who comes in with financial modeling skills can learn SQL and Python. You can order from almost any restaurant you want. How do we know what the merchant is going to do? What data are you collecting at each step? Come join us on this journey. *Copyright ODSC 2019. By building the last-mile delivery infrastructure for local cities, DoorDash is bringing communities closer, one doorstep at a time. If we collect everything but people can’t search, it’s like finding a needle in a haystack. A data scientist will ask a few questions on how you crafted the solution and go through your thought process. they want to see how you can offer actionable insights to the business and use your data science knowledge to do so. .ehsOqYO6dxn_Pf9Dzwu37{margin-top:0;overflow:visible}._2pFdCpgBihIaYh9DSMWBIu{height:24px}._2pFdCpgBihIaYh9DSMWBIu.uMPgOFYlCc5uvpa2Lbteu{border-radius:2px}._2pFdCpgBihIaYh9DSMWBIu.uMPgOFYlCc5uvpa2Lbteu:focus,._2pFdCpgBihIaYh9DSMWBIu.uMPgOFYlCc5uvpa2Lbteu:hover{background-color:var(--newRedditTheme-navIconFaded10);outline:none}._38GxRFSqSC-Z2VLi5Xzkjy{color:var(--newCommunityTheme-actionIcon)}._2DO72U0b_6CUw3msKGrnnT{border-top:none;color:var(--newCommunityTheme-metaText);cursor:pointer;padding:8px 16px 8px 8px;text-transform:none}._2DO72U0b_6CUw3msKGrnnT:hover{background-color:#0079d3;border:none;color:var(--newCommunityTheme-body);fill:var(--newCommunityTheme-body)}

We chatted with members of each team (below) to find out what they’re working on, where they came from, and who they’re looking for. It is a dataset with drivers, delivery times, money made, and tips. Ownership is big. Our role is to build a view of the whole DoorDash world so people can be productive and make the best decisions without worrying about infrastructure. After that, I lead the analytics team at an analytics startup, and then I ran product and engineering at a software engineering boot camp for people of color and women. That’s the first thing I probed when I got the offer here and I was really, really impressed. We are helping everything go as smoothly as possible, from the time an order is placed to its arrival at your doorstep. Rajat Shroff, vice president of product, says DoorDash data also clearly shows the connection between accurate delivery predictions and customer loyalty. We work with people from all different backgrounds. How far away did she park? The process starts with: (1) An initial phone screen by a recruiter. What will people learn if they join the team? You can do whatever you want because there is such a need for everything. It's the job description. DoorDash It varies for each, but I’d say 20 or more. Maybe the customer is in the backyard and can’t get to the door for five minutes. This isn’t the type of work where you sit in the corner by yourself all day. That’s a ton of things! Analyzing the data using quantitative analysis to provide insights on how to best help business and product leaders understand user behaviors, marketplace dynamics, and market trends. It’s more like all the time. That way we can teach each other and learn from one another. I would look to come up with data backed recommendations for each of DoorDash's 3 angles of business (driver, restaurant, customer) on how delivery service can be improved (time of day, type of restaurant, who tips the most?). Build models for next-generation pricing and pay algorithms. I always look forward to going to work and solving problems. Full-Time. they want to see how you can offer actionable insights to the business and use your data science knowledge to do so. Here we have three sides (customer, Dasher, merchant) so one change has so many effects. comments. Analytics, and machine learning in particular, can feel like a black box, so members of BizOps need to be able to communicate clearly and present a strong business case to people who may not have math or engineering backgrounds. Data Scientist salaries at DoorDash can range from $142,574 - $183,355. On-demand logistics is a competitive business with tight margins, so even tiny efficiency gains can be the difference between success and failure. I took a circuitous route to where I am now and it’s a testament to how DoorDash offers opportunities for people to take on new responsibilities. Ask yourself what's a business question you can answer. You will be asked a few questions about projects and background related to data science. So it’s very important to get the prep and travel times right. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account.

When the COVID-19 pandemic significantly changed how people took their meal, DoorDash had to retrain demand prediction machine learning models. Sound understanding of statistics and machine learning theory. Partner closely with hiring managers and leaders to hire top analysts and data … You get to dig into the data yourself. Their focus is on experimental analysis, building recommendation systems and features, building pipelines for recommendations, designing marketing attribution and segmentation, and building sales models. Yeah, I can dig deeper into prep time. DoorDash. DoorDash San Francisco, CA. Did you end up working a lot with the engineering team because that’s what you were interested in, or because that’s where you were needed most? Sure. 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. In our model the first step we take is establishing an estimated prep time for the order. You start with the basics, working on different projects, trying different things out within the team. We’re looking for people with discrete skill sets within the larger field. Apply for Openings Today! And then, if you’re interested in another field, you could do this for sales, or marketing, or anything. Diversity provides us with different perspectives and new ways of looking at things — we’re definitely looking for diverse candidates. Although these three teams are separate and work independently, in some cases they work very cross-collaboratively. They’re doing great things. Transforming cities, changing lives. Wow, that’s kind of crazy. Software Engineer, Data Platform. What are your responsibilities, and who are you working with? That’s bad for the Dasher, bad for us, and bad for the customer. Finally, what are some hard problems you see on the horizon? Activating Intent: How Confluent is Energizing its Diversity, Equity, and Inclusion Practice, Two Standard Cognition Founders on Changing the Way the World Shops, VP International Richard Gregory on Taking Quizlet Global, Facebook — The Past, The Present and The Future, The intuition behind A/B Testing — A Primer for New Product Managers. Maybe that’s a given, but I don’t want to assume. We’re a small team, we’re growing very quickly, and you really get to own problems. sudoshirt features subtle designs to show your love for technology. ._3bX7W3J0lU78fp7cayvNxx{max-width:208px;text-align:center} When building a product roadmap, it’s critical that we calculate the impact of different product changes so we can prioritize where we dedicate resources. 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. Senior Technical Recruiter, Analytics & Data Science. Jessica also explained what it involved to launch the business in other cities. Now anyone with a tiny bit of Python experience can run machine learning. Proficiency in numerical programming languages (Python preferable). Looks like you're using new Reddit on an old browser. People get happy when they get answers right away. You could have a huge impact immediately.”. 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? Login About Careers Blog Design Engineering. We also have multiple customer types. How to rip drivers off, by stealing their tips. We say, “Okay. They’re committed to growing and empowering a more inclusive community within their company, industry, and cities.



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