When we established Stratus Data in 2018, we had both been working for several years as staff data scientists–long enough to know a few things. We knew we loved working with data and discovering what hides in numbers. We knew, from time on staff together at Optimizely, that we could make a powerful team of two. And we knew that working on just one company’s data was a fast track to itchy feet: once you’ve spent six, or eight, or eleven months on a given data set, even the most curious practitioner can start to long for a new challenge.
A lot of companies are not in a position to support a full-time data scientist, let alone a sustained data practice, but have more than enough data to fuel valuable breakthroughs.
One more thing we knew: there was work to be done. A lot of companies are not in a position to support a full-time data scientist, let alone a sustained data practice, but have more than enough data to fuel valuable breakthroughs. We envisioned unlocking the insights their data held.
And so we founded Stratus Data, throwing open our doors to a constant stream of new challenges. Consulting work of the type that we do means that we can remain forever in our favorite phase of data science, exploring new territory and finding big upsides, with the added bonus that we’ve become experts at detecting the patterns that extend across a range of business domains. We’ve trained hard in order to take existing data and use it to unlock the profits hidden in the noise, and we love to do it.
As business owners, though, we’ve also had to learn how to hire, in order to keep up with our expanding list of clients. You might expect that a data science consulting firm would hire, well, data scientists. That was our expectation, too.
At first.
The Unexpected Journey of Growing a Team
What we quickly realized was that selecting potential hires from the data science training pipeline–whether an undergraduate specialization or a postgraduate “bootcamp”–didn’t actually help us find the best people for our team and our style of working. Number-crunching and some programming chops are completely necessary, but we needed more than that in a teammate. We needed to find people with demonstrated perseverance, a proven ability to tackle open-ended problems, and the maturity and interpersonal skills that come from knowing how to work collaboratively with others.
It took a while, but we soon figured it out. The training pipeline we needed wasn’t the data science certificate grind, but it did exist.
The training pipeline we needed was academia–we needed the depth of the scientific researcher, not the breadth of the general data science practitioner. Both have their place, but for the type of problems we want to tackle, we need people who can get stuck in quickly and grasp the fundamentals of every client’s unique needs.
In retrospect, it makes so much sense: neither of us have any formal “data science” training credentials, but we both had all the skills we needed to excel as data scientists from our time in higher education, Derek as a doctor of electrical engineering and Charles in his advanced studies of quantitative finance and consumer behavior.
In many ways, “data science” is the proverbial new-label-on-old-wine, a new phrase developed to describe the intersection of existing practices like software engineering and statistics.
Crafting Custom, Future-Proof Solutions
with a Team You’ll Love to Partner With
The fact is, most people who can make it through a rigorous MS or PhD program have exactly the attributes we seek: the ability to take ownership of a big, complex, open-ended problem (like the ones faced by our clients) and see it through to the end; a drive towards excellence; and a trained orientation towards first principles thinking. They’ve also, by default, worked collaboratively and become practiced in communicating well, both within their own discipline but also, and more importantly for us, outside of it. They know how to express complex concepts in straightforward English.
We’re sometimes the last in a string of consultants that an executive team has employed to try to solve a tricky problem. Sometimes those problems have been deemed unsolvable; sometimes a lot of money has been thrown at an issue, without even break-even results. Other times, companies have tried to solve the problem internally and burned through hours of employee time without resolution. Frustrations are high; expectations are low. And consistently, we find elegant solutions. We set a new, much higher standard for what can be done. Not only that, we receive amazing feedback about the experience of partnering with our team.
When you work with Stratus Data, two things are guaranteed: you are going to get high quality results that have been carefully thought through and designed to be future-proof, and you will love working with us. Both are consequences of the way we’ve assembled this team. Our slate of Stratons are determined data science practitioners who pair self-awareness with humility, who are self-assured but always curious, not given to conflict but able to handle it with respect, who seek out hard problems and solve them. Our team cares deeply about our clients; when you work with us, we take the time to listen deeply, understand precisely what you need, and create a truly custom solution you can’t get from any other team.
That’s Stratus Data. We know that businesses don’t come out of a box. Your data solution shouldn’t either.