Building The Analytics Team At Wish Part 4— Recruiting
And finally, we’ll end with a section on hiring. I’m going to share our philosophy on recruiting and how to find strong candidates.
At Wish, we’re still in growth mode. For hiring this teams that if we’re aggressively expanding the team, there can’t be a ton of hand holding otherwise existing engineers and analysts will be bogged down with onboarding. New hires need to be able to ramp up in their role with a higher degree of independence. This means we have to hire engineers and analysts that are typically more experienced, or if new then very smart and can figure it out on their own.
I’ve given over 150 interviews to engineers, analysts, product managers at Wish. I’ll talk briefly about some of what I’ve learned, and how that relates to hiring a data team.
Who do you select to interview?
For new grads, the strongest signals are marks and a STEM education at a top school.
For experienced candidates, we look at the types of teams that they worked in. Did they work at companies with strong engineering or data orgs? What types of projects did they take on? Are they relevant? If not, were they challenging? How much impact did they have at previous roles?
Because hiring is so competitive, job descriptions and picking out candidates has to be done strategically. Having a mantra of hiring only unicorns that can do everything just results in a backlogged hiring pipeline. If unicorns exist, they tend to find you. For the rest of us mortals, there has to be some leeway in matching skills to roles.
But if you do have a unicorn in your pipeline, speed is crucial. These candidates stay on the market for as little as 10 days, so wasting no time and pushing them through is necessary to close.
Strong interviewers need to have flexible questions that react to signals from the candidates. If candidates signals weak analytical skills and the job requires it, then the interviewer needs to double down and dig into the harder parts of their questions. If there’s signal for weak communication, then then interviewer needs to start giving ambiguous problems and see how the candidate can get clarification.
Adjusting questions should be done in between the different panels as well. To get hired at Wish, candidates have to go through 1–2 phone screens, and 4 onsite interviews. Interviewers need to be fast with putting their notes, so that the next interviewer has time to adjust their questions.
Copious notes should be taken and shared. This reduces the chance that at the end of 6 interviews, we still have gaps in evaluating the candidate’s strengths and weaknesses.
Interviews must end on a positive note. Its an incredibly vulnerable experience for candidates, and there’s no easier way to burn goodwill and gain a reputation than to have asshole interviewers. We all know which companies are assholes. It doesn’t take much to receive this label.
Working With Recruiting
One thing I’ve learned is that hiring managers need to be very hands on with their hiring funnel. Don’t wait for recruiters to screen your candidates for you. For good candidates, push them through to the next stage, and schedule their next interview yourself if you have to.
Wish has a lot of data. Our app has millions of daily active users and sells millions of items a day. At any given time, we track hundreds of millions of packages around the world.
All this should make us one of the hottest companies to work for as a data person.
But it wasn’t the case when I joined. Everything was so painful to use and query. What should have taken minutes to do took hours, and what should have taken hours took days.
We changed that.
What we’ve been able to accomplish in the last two years has been nothing short of incredible.
We rebuilt the entire data infrastructure and built out a data team for one of the largest Internet retailers in the world. And we did this without having the company miss a beat. We changed the tires of a moving F1 car.
We’ve been able to attach analysts to major teams, product features, and programs, removing reporting and data analysis from the critical path.
And we’ve made data a pleasure to use and work from.
This is something I’m proud of. And I’m happy that I’ve been able to share this with you. Thank you for reading!
Come Work With Us!
I’d like to end with a recruiting note.
This has been a great journey. But this is still very early, and we need more help.
I lead the Merchant Platform data team. We’re the team that supports everything on the seller side of the marketplace — the portal that merchants use to manage sales & inventory, and see if they comply with our policies. We own strategic features that include Wish Express (our 7 day shipping program), and ProductBoost, our advertising platform. We also own the user facing shipping estimator.
Come join if you’re interested in high amounts of ownership. This means as analysts, you’ll be driving decisions that impact the company with large swings in terms of revenue (+1% or more). As engineers, you’ll be owning and building out the system that powers every data feature and policy on the merchant side.
We need several hires in every role:
- Data Analyst / Senior Data Analyst Own policies, programs and product features. Quantify the upside and downside of decisions and drive them to completion.
- Software Engineer — Data Data is used heavily in the seller portal. It powers all user facing dashboards, and our policy engine which gives infractions to poor performing merchants. Help build out data features and scale this system.
- Quantitative Analyst Work on the shipping estimates, a user facing feature that tells users an estimate of when products will arrive. Work on risk features and reduce merchant fraud.
- All Other Roles
Email me at firstname.lastname@example.org for a referral.