At first I did infrastructure work, some internal tools, that kind of thing. Practice system design using real projects. d3ad Yesterday. DoorDash Tell us a little bit about your background. Data also drives a culture of experimentation at DoorDash. That’s an awesome problem to be able to work on. We are responsible for all things data at DoorDash. All Rights Reserved, Data Scientist, Analytics (Multiple Levels), Senior Data Scientist, Analytics (Multiple Teams). ._2a172ppKObqWfRHr8eWBKV{-ms-flex-negative:0;flex-shrink:0;margin-right:8px}._39-woRduNuowN7G4JTW4I8{border-top:1px solid var(--newCommunityTheme-widgetColors-lineColor);margin-top:12px;padding-top:12px}._3AOoBdXa2QKVKqIEmG7Vkb{font-size:12px;font-weight:400;line-height:16px;-ms-flex-align:center;align-items:center;background-color:var(--newCommunityTheme-body);border-radius:4px;display:-ms-flexbox;display:flex;-ms-flex-direction:row;flex-direction:row;margin-top:12px}.vzEDg-tM8ZDpEfJnbaJuU{color:var(--newCommunityTheme-button);fill:var(--newCommunityTheme-button);height:14px;width:14px}.r51dfG6q3N-4exmkjHQg_{font-size:10px;font-weight:700;letter-spacing:.5px;line-height:12px;text-transform:uppercase;display:-ms-flexbox;display:flex;-ms-flex-pack:justify;justify-content:space-between}._2ygXHcy_x6RG74BMk0UKkN{margin-left:8px}._2BnLYNBALzjH6p_ollJ-RF{display:-ms-flexbox;display:flex;margin-left:auto}._1-25VxiIsZFVU88qFh-T8p{padding:0}._3BmRwhm18nr4GmDhkoSgtb{color:var(--newCommunityTheme-bodyText);-ms-flex:0 0 auto;flex:0 0 auto;line-height:16px} I think that’s awesome. 0 0. facebook twitter reddit hacker news link. My team helps design, execute and evaluate these experiments. I’m interested in the space. We have both at Interview Query. We love our customers, so that’s never acceptable. Let’s say I’m a data scientist and I’m interested in DoorDash, what would you want me to know about your team? /*# sourceMappingURL=*/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. When you own everything end to end, you can have a strong business impact. DoorDash tracks hundreds of variables to make sure a customer’s food arrives on-time and fresh, but the impact of data reaches well beyond the product. .LalRrQILNjt65y-p-QlWH{fill:var(--newRedditTheme-actionIcon);height:18px;width:18px}.LalRrQILNjt65y-p-QlWH rect{stroke:var(--newRedditTheme-metaText)}._3J2-xIxxxP9ISzeLWCOUVc{height:18px}.FyLpt0kIWG1bTDWZ8HIL1{margin-top:4px}._2ntJEAiwKXBGvxrJiqxx_2,._1SqBC7PQ5dMOdF0MhPIkA8{height:24px;vertical-align:middle;width:24px}._1SqBC7PQ5dMOdF0MhPIkA8{-ms-flex-align:center;align-items:center;display:-ms-inline-flexbox;display:inline-flex;-ms-flex-direction:row;flex-direction:row;-ms-flex-pack:center;justify-content:center} I started out doing a lot of everything, to be totally honest. We talked about what data science is, and examples of how it is used at DoorDash.

I’m working on the dispatch team right now, which boils down to the execution of deliveries. “We are helping everything go as smoothly as possible, from the time an order is placed to its arrival at your doorstep.”. How far away did she park? I want to hear what my team members think is a problem. If somebody joined the team right now, what would they work on? That could mean making sure deliveries arrive on time, making sure that you’re consistently getting exactly what you ordered, making sure we have the best selection of restaurants on the platform, and much more. Why is this an interesting challenge to you? 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? As soon as you make a better model for food prep time, for example, Dashers are more efficient and they make more money.

DoorDash is aware of the importance of data and the need for a high-energy, confident, and well-experienced data scientist. Full Time This is a burger and shake. ODSC hosts one of the largest gatherings of professional data scientists with major conferences in the USA, Europe, and Asia. We have this really fast analytics database where the data is, of course, still messy because real life data is always messy, but you can run machine learning on it right out of the box.

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