Data Science Challenges at Instacart

I&#x27;m all for data science, but I think this article romanticizes the field a bit too much (the random pictures of employees being thoughtful do not help).<p>Listing the problems Instacart has which can be solved through applied statistics is one thing. The other, more important thing, is <i>how exactly data science works</i> to solve problems, with technical detail, as opposed to data science being some mystical unexplained power. (Especially since the intended audience for this post is data scientists Instacart wants to hire and presumably are already knowledgable in data science)

My friend works at Instacart, she says it totally sucks. Mainly because there&#x27;s no guaranteed hourly pay and what she gets paid depends entirely on what the customers decide to give out as a tip. So for example sometimes she spends an hour grocery shopping for someone, then 30 minutes to drive to their house, and then the person can just arbitrarily decide to tip her $10. So she makes $10 for 90 minutes of work? That&#x27;s below minimum wage. She says MOST people will tip $20 or $25 however not all. So all it takes is one cheapskate to not understand how long it takes for you to go and pick out and deliver all their groceries and you get totally screwed.<p>Apparently when she works in SF proper is the only place with a guaranteed wage, all surrounding areas you are at the mercy of what people decide to tip. Sounds outrageous, all it takes is one cheap idiot to completely ruin your shift and make it not worth it to work there.

On this note: My team and I at Analytical Flavor Systems[1] wrote a blog post on how we go about hiring data science interns[2]. It&#x27;s heavier on the technical details, and suffers from less... romanticism....<p>[1] www.gastrograph.com<p>[2] <a href="https:&#x2F;&#x2F;gastrograph.com&#x2F;blogs&#x2F;gastronexus&#x2F;interviewing-data-science-interns.html" rel="nofollow">https:&#x2F;&#x2F;gastrograph.com&#x2F;blogs&#x2F;gastronexus&#x2F;interviewing-data-...</a>

we are hiring would have been enough.

&gt; <i>Many of our best data science ideas have come from Instacart employees in the field – working directly with our shoppers in our stores, or interacting directly with our customers.</i><p>I have no idea what this could mean. Either you&#x27;re getting algorithm suggestions from your shoppers and customers, or (more likely) &quot;data science&quot; means &quot;user interface.&quot;

So we no longer use the word scientist to describe people who do science? What a shame, I think science is really neat and scientists deserve unique respect.<p>As far as I can tell what these people do every day is called &quot;business&quot; or maybe &quot;logistics&quot;

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Behind the scenes, Instacart is a revolutionary new e-commerce marketplace, an incredibly sophisticated last-mile logistics engine, and a dynamic source of work for thousands of personal shoppers. Each of these aspects of Instacart could be a whole company elsewhere, and data science plays a key role in our success in each endeavor.<p>In this article, I (our VP Data Science) highlight some challenges the data science team is tackling at Instacart ranging from logistics to personalization. I also go into detail on how we have organized data science to have maximal impact and what we look for when recruiting data scientists.