I generally think that my preparation of a company before I go into an interview is a distinguishing and standout trait.

Note quickly that this post is United States centric in how certain objectives are achieved, but some of the techniques might generalize for an international audience.

There are usually two smallish moments in any interview where this really helps set a tone though. These smallish moments can be incredibly beneficial though, especially for data science interviews because it spells out several incredibly important traits about you as a candidate: 1. You are a person capable of doing research 2. You are genuinely interested in this company (or alternatively, you’ve been following this company and waiting for a moment to strike) 3. You care 4. It instantly creates rapport with an interviewer. Companies are like conspiracies that employees are all in belief of (to paraphrase an investor/attorney). By knowing a bit more of the ins and outs of a company, you seem instantly like a co-conspirator

So, how to unlock this research? Let’s go.

There’s two sides to company research in my opinion. First off, a company is (both legally and conceptually) like a person, so you need to research the history of the company: origin story through where they’re headed. Secondly, a company has people who work in it. Those people have values and beliefs that generally align with the company, but they are also individuals, so at the very least, we want to get to know figures in two areas: 1. The team you’ll be working with (hopefully it’s a proper data team) 2. The leadership team or whomever sets the vision for the company.

Researching a Company

It’s kind of amazing how close free research tools and professional ones are these days. The only thing limiting you is your creativeness in searching. The first place I go to check out is the company’s actual website, where I do a quick readthrough of the “About” section. Then I check out the bottom of the page which usually contains the legal entity name of the company (if not, try looking around a bit, it’s usually also somewhere legalish like privacy policy, or terms and conditions. Knowing this will lead us to our next spot: the Secretary of State site.

Let’s say the company’s HQ is in California. I’ll run a simple Google search for california secretary of state business search and usually one of the first hits is a website featuring that person’s name. As of writing, that name for California is Alex Padilla and that website is https://businesssearch.sos.ca.gov/. Then, using the name you found on that website, search for a company. The first thing you’ll likely see is the articles of incorporation. Check out who some of the people are in this document, and run a search on them. These people might still be at the company in leadership positions or not. Either direction is telling.

You might also find that the company is under the umbrella of another company. You can search for these parent companies and get a better understanding of the organization.

The next thing I’ll do is check out the company on Crunchbase and Angellist (if they’re on there). I do think to get a better understanding of which investment stage that company is at, along with an understanding of who the leadership and/or founding team is important for two reasons. 1. Seed stage companies are usually entirely different in approach and necessary functions performed on the job than companies at Series B or C. Some things overlap with all startups, and in our contexts as data scientists, it means that you’ll likely be doing work outside of the scope of quote data science work proper end quote. The degree to which that is true is also correlated to the maturity of that company and the investment stage is a sort of leading indicator of that maturity. 2. How a company thinks about raising capital is important. Are they revenue positive and they are using cash as a sort of “turbo booster” for that growth (for imagery people think: they are a roaring fire and investment is gasoline)? Or are they marching towards an exit (if so, things like job titles might start to matter… congrats on your super fast promotion to “Lead Principal Director of Data Science”)? Or are they raising money because they aren’t cashflow positive nowwww but they are projected to be in the future? All of these can help you in negotiating salary/equity. They can also help you get a sense of how sophisticated the leadership team is in navigating startup world. Then I’ll check out team. Since I’m usually already in the realm of the leadership team, I’ll do quick checks on them. I think kind of like constructing the biopic of that team. If I was writing a movie on the founding team, what does it look like? Were they friends from a young age and then found something that they do well together? Were they at some other company and then realized some fundamental truth before anyone else so they spun off and made their own thing? I worked at a startup called Alo Yoga and their origin story was pretty neat - two friends from high school started a tee shirt printing company. Through a series of smart moves: realizing that blank shirts for other printing companies was where the money was at; realizing that their athletic line was doing well; then realizing that their high end, eco friendly yoga wear line was doing extremely well; then spinning that off into its own entity, they formed an extremely popular and strong brand with Alo. This makes for a compelling narrative. This also meant that the founders both had incredible domain expertise in retail and fashion, since they were born in that crucible and had lived there for more than a few decades. This ripples out: people around me had degrees from FIDM and design schools, high fashion and high art types! The next thing I’ll do is check out the team that I am likely to be on. If the company has a proper Data Intelligence team, then this part is a little easier. Since Data Science as a discipline crosses several functional areas of a company though, you may be embedded in any of those functions. The first thing I’ve done for the past few years is run a LinkedIn search for data engineers. If a company doesn’t have one, you’re going to be standing up databases and writing ETL pipelines. Maybe that interests you, or maybe you are more interested in creating models. The next thing I’ll do is check to see if there are other data scientists. What do their backgrounds look like? Are they similar to mine? What kind of tech stack does it look like they work with and have prior experience in? Are they and R shop or a Python shop?

I have lately also been using Wappalyzer as a way to look up technologies that a company uses. This website sends back an analysis of tech stack, although primarily it looks like front end stack, there might be some clues to how data gets from the front end to your database.

They last thing I do is check out the values/mission type stuff of the company. I like to save this for last, because at this point, I’ll have already formed a narrative in my head. Sometimes these are in alignment, and sometimes they aren’t and both are telling.