Though the world at large has caught up-to speed with AI, automating most customer-centric business processes, in the words of Dr. Michael Li, founder of Washington-based Data Incubator, hiring still remains “stubbornly unscientific”. San Francisco-based Airbnb seems to have perfected the art of hiring data scientists, laying down their best practices for companies hiring data scientists many a time in public forums.
Airbnb’s head of Analytics, Riley Newman, had redesigned their interview process and recently factored in gender balance and homogeneity in the team as well. According to Newman’s old post, How does Airbnb hire data scientists – the company laid down their best practices in hiring data scientists in four steps. In a nutshell, their right candidate is one who work in collaboration with their business partners and is well-aligned with the company’s mission and core values as well.
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- Resume/phone screens: The company is always on a lookout for people with sound data analysis background and have working knowledge of Airbnb as well.
- Basic data challenge: Prior to a face-to-face interview, the candidate’s are given assignments to validate their work — few datasets as an exercise to measure their capabilities before taking the interview process forward.
- In-house data challenge: on the big day of the interview, the candidate gets an in-house challenge, using real world data and solving everyday problems that the company’s data analytics team is tasked with. A wholly transparent process, it also allows the candidate an opportunity to understand how the team works. At the end of the day, the candidate is expected to present his findings to the team. He is also assessed on how he/she built a model and presentation and communication skills.
- Once the in-house challenge is cleared, the candidate is in for a long haul with four interviews – two with business partners and two less technical to assess whether the candidate is aligned with company’s core values.
At a time, when there is a raging talent war for data scientists to bolster teams, nabbing the best talent has become a tricky job. Since data science is a growing field, many a time, startups and companies do not have a set process for hiring. Though there is a surge in interest in the field, the growth of demand outpaces the right talent. Analytics India Magazine presents some best practices to bear in mind before hiring data scientists.
Know your needs: Ideally, the role of a data scientist is to solve key business challenges. Businesses should understand their requirements and the core competencies required to ensure data scientist are able to develop models that support and benefit enterprises.
Broaden the pool of search: according to Glassdoor, data scientists are the most in-demand jobs across the world. The explosion in interest aside, there is a gaping talent gap — McKinsey Global Institute estimates the shortage of data scientists in 2018 at 190,000. The numbers aside, good talent is hard to come by and not every candidate will be backed by experience. According to Deloitte, leading IT consultancy, most universities and colleges aren’t able to produce data scientists fast enough to keep pace with industry’s demands. And you can’t get experienced data analysts from two-year or one-year certificate programs. Hence, it is advisable to widen the pool with freshers and providing in-house training.
Look for business/domain understanding: The right candidate isn’t one who can sift through petabytes of data but also know how to apply analytical models in a user-friendly way in reports. Analytical skills aside, what’s underpinning the role is solid business understanding as well.
According to Robins, business understanding is key to unlocking the data science boom, a field that has become jargon-heavy.
“The secret of the ongoing “data science” boom is that most of what people talk about as being data science isn’t what businesses actually need. Businesses need accurate and actionable information to help them make decisions about how they spend their time and resources. There is a very small subset of business problems that are best solved by machine learning; most of them just need good data and an understanding of what it means that is best gained using simple methods,” he shares.
Some people will argue that what I’ve described as being valuable isn’t “data science”, but is instead just “business intelligence” or “data analytics”. I can’t argue with that arbitrary definition of data science, but it doesn’t matter what you call it — it’s still the most valuable way for most people who work with data to spend their time, notes Robins who reveals he receives a number of queries from people who want to get into “data science” seeking advice. Should they get a master’s degree? Should they do a bunch of Kaggle competitions?
“My advice is simple: no. What you should probably do is make sure you understand how to do basic math, know how to write a basic SQL query, and understand how a business works and what it needs to succeed,” he points out.
Is Kaggle worth it? According to Robins, “If you want to be a valuable contributor to a business, instead of spending your weekend working on a data mining competition, go work in a small business. Talk to customers. Watch what products sell and which ones don’t. Think about the economics that drives the business and how you can help it succeed more”.