Data Science Success Story: Re-Tooling Mid-Career

Data scientist Alex Dang used Interview Query to keep his skills and knowledge fresh in between jobs. After facing a layoff from a rideshare company, he found himself in the competitive pool of candidates for his next opportunity in the industry.

The experience helped him learn about current hiring trends, how to make himself a better candidate, and how to leverage your network and online tools to improve your chances!

Learn more about how his experience can help you in your own journey!

When I found Interview Query, I wasn’t a fresh undergrad, I was already mid-career. I graduated from Cornell in 2015 with a degree in Operations Research at a time when data science wasn’t a big thing yet. I had an interest in computer science, coding, and programming, but I decided to center my studies around statistics. In operations research, I focused on things like optimizing casting processes.

After undergrad, I consulted for a year, but it wasn’t all that interesting to me to build presentations in a non-technical role. That’s when I made the choice to go back to school. I attended Columbia for three semesters to earn my Master’s, and I stayed focused on statistics. While studying, I took an internship at LinkedIn and accepted its return offer, so I moved from New York to California.

I spent one year at LinkedIn before jumping to a pre-IPO Lyft and spending four years with the company. Lyft started its layoffs during COVID, and on the third round, I lost my job. Even with my experience and looking for roles in the same industry, it took me five months and several referrals to receive my current offer.

Looking for a job was a shock - it was a wake-up call. Competition for every job is high, and you must set yourself apart from other candidates.

Candidates must have domain knowledge and focus on relevance. Candidates should also focus on what they’re good at and where their experience is instead of trying new things. A few years ago, you could try new things and not be an exact skillset match, but it’s not like that anymore.

For example, I talked with an Uber recruiter, and the role focused on pricing. Since I hadn’t worked on pricing at Lyft directly, I didn’t get that interview for Uber.

In another example, I applied for a role at DoorDash, but the role was for a new product launch focused on its merchants. The questions focused on products and business. Even though I gave them good answers, someone who has worked on merchant product launches would give better answers.

To get over this challenge, I preferred to use referrals to find my next job. I reached out to my network, friends, and people I went to school with whom I may not have talked to in a while. Even though I had referrals, the referrals didn’t guarantee an interview. When many roles have 100+ applicants, it can come down to fit more than just meeting the basic requirements.

LinkedIn was a great tool for this task and allowed me to reach out and ask for referrals from my network of classmates and colleagues. My current job did actually come from a referral from an ex-coworker. You can also use LinkedIn to reach out to recruiters and hiring managers, who will hopefully put you in touch with internal recruiters.

I focused on applying for data science jobs, but even data science job interviews consist of other components like coding and programming. What I witnessed is that data science job interviews have become standardized.

Most interviews have a first-round phone screen, first interview, and final round. Some tidbits of advice:

• When you talk to a recruiter, it’s not an interview. Talking to a recruiter is a phone screen that judges you are, at a minimum, worth the team’s time.
• For data science job interviews, there is always an SQL question, product case questions, and product analytics problems.
• For SQL questions, companies want to gauge your understanding of it; they test if you can write it on the spot.
• In product analytics, candidates will work with experimentation or explain business problems.
• Companies want to find out every candidate’s business sense and how well they understand the industry.
• Interviewers will ask questions that cover concepts, and there will be “gotcha” questions. A fresh grad might remember the book answer, but you also need work experience to make the answers relevant.
• During interviews, you can’t miss anything, and every question or gesture is an opportunity to signal who you are and the value you bring to an organization.

In the interest of not missing anything, Interview Query helped me gain confidence that I had the breadth to answer any question.

IQ’s platform meant that I could study key concepts concisely without going through a textbook because the platform provided quick five-minute reads. The advantage of Interview Query is that it’s not super theoretical like textbooks, but it’s more organized than other sites.

IQ’s articles are well-written by someone who actually has experience in the subject. Having been in grad school and having read a lot of long-winded pieces, I appreciate when articles are direct and place concepts one after the other. The material always had a good level of detail, mentioning all the necessary components and linking them to relevant subjects.

After my layoff, I went through ups and downs. I set small goals every day to keep me grounded. Goals help you stay motivated. So, every day, I would read two articles on A/B testing, for example, or get updated on the latest trends in machine learning.

I never stopped learning and preparing.