6 min read
It's hard to stand out as an online content creator these days. The web is awash with influencers, thought leaders, content marketers and what we used to just call bloggers. So it takes something pretty special to grab my attention.
But that is what happened when I read an article called The Analytics Engineer on the Locally Optimistic blog. The writer, Michael Kaminsky, nailed the job description at the intersection of data engineering, data analysis and data scientist which is something I've been trying to do myself for quite a while. And it's no mean feat.
I liked his take so much I immediately emailed Michael to see if he'd do a Q&A for me. And, I'm very pleased to say, here it is.
Tell us a bit about yourself, how did you get into the data space and what does your data journey look like so far?
I got into data through economics and econometrics -- I was lucky enough to have the opportunity to do research with Kerry Smith at Arizona State University as an undergrad where I learned how to use the statistical software package Stata to do econometrics research (I even was able to co-author a paper as a result of some of that research).
After undergrad, I joined a boutique economics consulting firm where I wrote a lot of SAS code to do research for pharmaceutical companies -- that was a nice blend of academic research and industry. After a while, I realized that I really enjoyed the more technical aspects of the work.
I wanted to focus on developing more software engineering skills and becoming a "data scientist" (which, at the time, was still a very new term).
During your analytics career, how did a typical day look for you? Which tools and languages did you use? Big team / small team / lone wolf? Remote / office-based / co-working space?
I've had the pleasure of getting to do a lot of everything. There have been months of writing nothing but SAS code and building excel models, months of just R, and months of just python.
My favorite toolset is my Vim + Tmux setup which I use as my core IDE for whatever I'm working on -- normally some mix of SQL, Python, and R.
I've generally always worked on small teams (3-6 people) as part of a larger organization. Historically always in-person, though last year I moved to Mexico City and have been working on building two startups from there, completely remotely.
Now you have transcended into a startup founder role with two startups, Bolster and Recast.
How does that differ on a daily operational level and how do you find the skills you nurtured in analytics have crossed over to your new role?
To be honest, it's hard to underscore the difference in the type of work that I'm doing.
Bolster is a digitally native brand that helps people with long-term care planning and involves almost exclusively marketing and operational tasks, and Recast is a data science product for marketers (imagine a new-and-improved media-mix-model).
What I've learned is that starting a new company takes way more skill in marketing and sales than I ever would have imagined. And sales is hard.
I spend a lot of time just talking to people trying to learn how they think about a product or a problem so that I can figure out how to position the product in a way that's most appealing -- I would have never guessed how much time I'd have to spend on marketing and sales when I was first nursing the idea of starting a startup a year ago.
The most important skills that cross over to building a startup are the critical thinking and numeracy skills I developed as a data scientist. When you're starting something new you have to be super-disciplined when it comes to making decisions and evaluating the strategic opportunities in front of you.
One thing I'd like to expand on (and it might just be because it also applies to me at the minute) - how do you find splitting your attention between two startups when all of the conventional wisdom is to Focus Focus Focus?
I don't have a great answer, and I actually have a lot of self-doubt about this. On the one hand, having more irons in the fire seems like a reasonable way to decrease risk and increase the chance of a "hit".
However, as I've gotten deeper into these projects I've started brushing up against the point where I truly have too much work to do and the divided attention really does cost me something (and closes off some opportunities).
When I was just getting started, I had plenty of time (and a lot of the work was waiting on things to happen so even with both projects I had some light days / weeks). But now I've got a more-than-full plate and am having to make real trade-offs about where to spend my time.
I expect that if / when one of the projects gains significant traction I'll have to put the other on the back-burner (or kill it altogether) -- it remains to be seen whether or not I'll have the discipline to do that when the time comes!
I'll have a better answer for you on whether or not this strategy was a good one after another year and we'll see where I am :)
You are a writer and blogger and it was your article “The Analytics Engineer” that caught my eye when I first read it.
How important do you think it is for data professionals, at all stages of their career, to share publicly what they are doing and learning?
I think writing regularly is one of the best activities you can perform to boost your career. Most importantly -- to write well, you have to think well. The act of writing helps me clarify my thoughts on a topic, and personally I've observed that writing more has helped me clarify my thinking generally, not just when I have my hands on the keyboard.
The practice of clarifying your thoughts for a blog post or article has carry-over effects that make your thoughts clearer in other domains. I cannot encourage everyone enough to write more!
The second benefit is the professional network you can develop through sharing your writings -- from this Q&A to the entire locally optimistic community, I feel super lucky to have gotten to meet so many super talented analytics professionals.
I'm 100% positive that my next full-time job will come through the locally optimistic community, and I wouldn't have that if I wasn't regularly sharing my thoughts through the blog and our Slack channel.
Where do you feel your own career will go next? Can you ever see a point where you will move back into analytics full-time?
Or do those analytical skills now just become one of the fundamental building blocks of making you a better overall entrepreneur?
Gosh I wish I knew the answer to this.
I really like being an entrepreneur, but I miss "earning money" and "not working every weekend". The truth is that I can totally see myself going back to a full-time analytics role if none of these entrepreneurial projects work out -- however, I believe that the last year has been such an incredible learning experience for me that I'll be a much stronger analytics leader if and when I join another organization as an FTE.
The lessons I've learned about business strategy, product-building, and sales will surely be valuable in whatever organization I join next.
Your own career path has taken you through non-profits, fast-growing B2C businesses like Harry’s Grooming and now your own startups.
Do you think that made you a better analytics all-rounder rather than, say, sticking in one industry and diving deep into that specialisation?
I think it all depends on what you want to get out of your career. I really treasure variety and take a lot of pride in using expertise from other disciplines to bring unique insights into whatever I'm working on.
While this works super well for me, I know that I'm not as skilled in one discipline as lots of other people are -- there are lots of things I can't do (and professional trajectories that are closed to me) since I've taken this path.
I'm never going to be a great machine learning or algorithms engineer, and I'd be wayyy behind if I wanted to get back into academic-style research.
However, I think my broad knowledge base and general interest in pretty much everything is one of my super-powers, so I've leaned into it.
If you had a list of “best-kept-secrets” (websites, books, coaches) that have helped you, which would you recommend?
Some things I do that I think more people should do:
What is the number one piece of advice you give to aspiring data professionals?
"Work to create as much value as you possibly can."
There are a few parts of that sentence that people need to answer for themselves, but the core of the sentiment is super important.
You need to figure out what "value" means in your particular organization (or to yourself!). You need to figure out how you can actually create that value.
And, most importantly, you have to go out and actually do the work!
Lots of data professionals are really interested in "research" and "working on interesting problems" -- the problem is that "doing research" doesn't necessarily create value!
If you want to find success in whatever organization you're in, a single-minded focus on how your efforts can create more value is the best way to do it.
Where can readers find you online?
Email is the best way to get a hold of me: email@example.com
And I'm pretty active on the locallyoptimistic slack channel -- learn how to get an invite here.