Academia to Data Science

AirbnbEng
AirbnbEng
Oct 19, 2016 · 7 min read

By Avneesh Saluja, Alok Gupta, Cuky Perez

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Introduction

However, taking a quick glance at the Ph.D. labor market shows that the number of doctorate-holders we produce annually exceeds the number of positions available. Also, Silicon Valley (and the tech industry in general) has been an appealing destination for many former academics and those with a research bent, but the leap from academia to industry is not an easy one. Michael Li documents the mindset shift required in one of his recent blog posts, and frames this shift within the context of delivering business-impactful results quickly (in industry) compared to delivering perfect results (in academia).

While we agree with this basic trade-off, at Airbnb we feel that the mindset shift is slightly more nuanced. In this post, we first discuss the skills (both hard and soft) we look for in candidates hoping to transition from academia to Airbnb, followed by specific pieces of advice for those looking to move into the fast-paced startup world of Silicon Valley.

4 things we look for in Data Scientists from Academia

1. Beginner’s Mindset

2. Self Starter

3. Talking the Walk

4. It’s a Sprint not a Marathon

4 tips for Academics interested in Data Science

1. Sweat the Small Stuff

Unfortunately, this is not the case in industry. A lot more thought and creativity has to go into how to set up the problem in the first place. Are the labels that we extract or derive from logged data the signals that we need to solve our problem? Are there any bugs in the instrumentation? Are we even logging the information we need? Data Scientists need to understand the problem domain deeply and use that information to transform both the problem and the data to be able to produce something meaningful. Getting things to work in these fast-moving environments often requires as much, if not more creativity, as an abstract research problem.

2. The 80% Rule

Given these realities, it’s advisable in these situations to deploy a model that isn’t perfect, but is “80% of the way there”. Optimizing for the remaining 20% of performance oftentimes reflects not only a lack of prioritization (“premature optimization”) but may also simply be not possible given the disconnect between intrinsic and extrinsic evaluation metrics.

3. The Knowledge-Impact Sweet Spot

4. Being Proactive

To rehash a well-known quote: “it doesn’t make sense to hire smart people and tell them what to do; we hire smart people so they can tell us what to do”. We hire people from all kinds of academic backgrounds and qualifications precisely because these different vantage points provide a unique array of tools to tackle the kinds of interesting and challenging problems we face at Airbnb.

If you are interested in learning more about Data Science opportunities at Airbnb then you can visit our careers page here.

Check out all of our open source projects over at airbnb.io and follow us on Twitter: @AirbnbEng + @AirbnbData

Airbnb Engineering & Data Science

Creative engineers and data scientists building a world…

AirbnbEng

Written by

AirbnbEng

Creative engineers and data scientists building a world where you can belong anywhere. http://airbnb.io

Airbnb Engineering & Data Science

Creative engineers and data scientists building a world where you can belong anywhere. http://airbnb.io

AirbnbEng

Written by

AirbnbEng

Creative engineers and data scientists building a world where you can belong anywhere. http://airbnb.io

Airbnb Engineering & Data Science

Creative engineers and data scientists building a world where you can belong anywhere. http://airbnb.io

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