My headphones are in, and I’m listening to Jóhann Jóhannsson’s The Miners’ Hymns — one of my favorite albums for coding. I’m finishing up an API for our new Heads Up Display (HUD), for which I’d worked out the math a few days earlier. I had spent the previous day figuring out how to implement the math and testing out edge cases with synthetic data, interspersed between product planning meetings and debugging a performance issue with a new component of the HUD backend. I’m about to put out a Pull Request, when I take a look at Nagios and notice that one of the systems that powers the current HUD has just gone critical. I start to debug, and a second later I get a Slack message from someone on Chartcorps saying that customers are starting to notice that the HUD is down. I see that it is a simple fix this time; I just have to restart one of services that powers the HUD.
Just in time, too, because I have to head uptown with members of our sales team to talk to one of our strategic clients about our new headline testing product. On my way out the door, one of our designers pulls me aside to look at the current designs for displaying the results of headline tests in the new HUD: “Does this viz accurately represent the data?” We talk for ten minutes, weighing pros and cons and looking at design alternatives. We talk about color schemes. We talk a bit about user interaction.
The meeting uptown goes wonderfully; I give a high-level overview of multi-arm bandit headline testing, answer some technical questions about the product, and get great feedback about the product to take back to the team. When I get back in the office, I see a message from Lauryn Bennett, our Head of Brand, asking if any of us on the Data Science team have time to answer a request from a journalist about a news event that just happened. This particular request doesn’t require an in-depth statistical analysis, so I write a quick script to pull the numbers. I spend a bit of time looking at the results and then write up a few paragraphs describing what I’ve found. I then head into a meeting with fellow engineers, designers, and product owners to plan our next Sprint.
This is my typical day.
Download now: Chartbeat Insider Guide: How to use Headline Testing to Hook and Hold Readers
#DataScience
According to the Harvard Business Review, data science is the sexiest job of the 21st century. If you have data, you need a data scientist to get value from it; data scientists are the only ones who can wrangle #BigData into submission. Apparently, data science will save us all.
I’ve read many pieces over the past year trying to describe what data science actually is. There’s usually some talk about math and programming, machine learning, and A/B testing. Essentially these pieces boil down to one observation: data scientists do something with data. #DeepLearning anyone? I’ve followed arguments on Twitter and blogs about who should and should not be considered a data scientist. Is Data Science even a new discipline? How does it differ from Statistics? Programming? Or is it this…
¯\_(ツ)_/¯
Ok, then, what the hell does a data scientist actually do?
Now this is a question I can answer. And since I haven’t read many concise descriptions of what data scientists do day-to-day, I figured that I’d throw my hat into the ring and talk about the kind of data science we do here at Chartbeat.
“WARNING: it may be a bit different than what you might have heard that data scientists typically do for a living.”
OK, so, what exactly do you do?
Our team here at Chartbeat are what I like to call Product-Centered Data Scientists — meaning the majority of things we do on a daily basis are in direct support of our products. Because we are a data company, our role is pretty central to the organization. Of course, we do math. We build data pipelines and write production code. We do all kinds of analyses. But we also work regularly with sales and marketing. We go on customer visits and help out with sales calls. We even participate in user research with our designers, UX, and product owners.
Engineering
As a tech company, we build software products. Plain and simple. As a data company, every one of those products has a data science need. Because of this, our team is embedded within the engineering team, and most of us take on heavy backend or front-end roles in putting code into production. We don’t just hand prototypes over to engineering for them to implement. We do the implementation. We tune our Redshift clusters, find API performance bottlenecks, choose the proper data structures. We are also part of the backend on-call rotation. If Chartbeat were to break at 2AM, we’d help fix it.
For example, just consider our Engaged Headline Testing tool. Andy Chen and Chris Breaux have been instrumental in designing, building, and maintaining the systems that power headline testing. Andy worked out the initial math for adding Engaged Time into the multi-arm bandit framework and was one of two people who built the initial backend. Chris Breaux has since taken over the Data Science role on the team and continues to push the math, and the product, to new places. The new features that will be released soon in that product are — in no uncertain terms — data science features.
In fact, all of us play central roles to each of the products with which we are associated. Josh Schwartz and Justin Mazur have built an enormous portion of our Ads Suite, Kris Harbold and Josh have built all of Report Builder, and Kris holds the distinction of being our only team member to have both front-end and backend code in production. Justin and I have worked on our Video Dashboard, and I’ve built a lot of the HUD. Each of us has contributed countless lines of code to all sorts of systems across Chartbeat.
“I don’t think it is an exaggeration for me to say that there is not a part of Chartbeat code that a data scientist has not touched.”
Math
Okay, so we do math. This just comes with the territory. Sometimes sophisticated math, sometimes rote math. This math is either in direct support of a product or is part of an analysis we’re working on. We don’t do math every day, but when math is needed, we are there to answer the call.
Research + Analysis
Analysis is typically thought of as an essential skill of a data scientist, and we definitely do our fair share. These analyses range from customer specific reports to industry-wide analyses to analyses that inform a specific product build. Take, for example, the analysis Chris Breaux did on Dark Social traffic, or the countless studies Josh Schwartz, our Chief Data Scientist, has published on our blog and elsewhere. Or take, for instance, the research that Justin and Chris recently did towards updating our Engaged Time measurement methodology, the work Kris and I published on user journeys through websites, or the work Jeiran Jahani is doing to break new ground in topic detection. If there is a question that we can use our data to answer, we’ve likely been tasked with answering it. Sometimes our analyses take a few minutes; sometimes they take a few weeks. Sometimes we have to dig deep into our bag of tricks and pull out sophisticated statistical tools. Sometimes we write simple SQL queries to calculate averages.
User Interviews + Ethnographic Research
With our product designers and product managers, some of us on the data science team sit in on user interviews and do ethnographic research. This is not something that I’ve seen as common to data scientists at other organizations, but I think it is an incredibly important activity for a product data scientist to participate in.
I know a lot of data scientists and engineers who roll their eyes at this kind of stuff, but understanding user goals helps in the design of a data pipeline, the choice of an algorithm, or the decision for which metric is best for a given application. It makes you empathetic to your user base, which is never a useless endeavor. What product-centered data scientists do is try to keep in our heads at all times exactly what has to happen to create an amazing user experience.
“From the ugly, messy data at the start of the pipeline, to the user’s interaction with the tool, the user is interacting with data, and that has to be in our purview.”
These interviews also give context for where you can be lax with assumptions, because you often have to make trade-offs when you try to implement your fancy models. Sometimes all that great math adds one second to the response time of an API, and when you have traffic like ours, sometimes you can’t afford one second. Knowing the fidelity that your users expect or require helps solve this problem.
When we were redesigning the HUD, I sat in a variety of newsrooms with one of our designers and watched editors work. We simply watched them use our product in their day-to-day flow, and asked questions now and again about what they were doing. I also sat in a few user interviews during this time and have since sat in on countless others. Those experiences have influenced the engineering and data design of the HUD, as well as several other products I’ve helped build. And now, I can’t imagine being part of a product build without having done at least some user research.
Ideation + Future Products
Product-centered data science is not all about maintaining current systems or developing feature increments. There is also a large amount of long-term vision thinking. What will our products look like next year? In the the next five years? Often, our team will prototype systems to test feasibility of an idea or a product direction. We comb through the latest data science papers and computer science literature to see if any of the latest findings can be applied to future (or current) products. Once every six weeks, we set aside a week for our entire team to do data specific projects that aren’t directly connected to current projects. We’ve built some cool stuff — a system that scrapes and searches content across our network, a tool that discovers popular stories in the news, a deep recurrent neural net to predict traffic, a Slackbot recommendation engine — you name it.
Sales + Marketing
Not only do we help design and build the products, but we do what we can to help sell them, too. We’ll often pull customer-specific numbers, industry benchmarks, or even do full-on reports for the sales team to use on on sales calls. Sometimes we’ll even sit in on those calls and other client visits. We write blog posts and our Data Science Quarterly, which help out the marketing team grow our customer base. We write product white papers. We give interviews to reporters. Basically, we are tasked with speaking to whomever on behalf of Chartbeat Data.
Product-Centered Data Science
This is product-centered data science — at least here at Chartbeat. Personally, I think every product team should have a data scientist on it. Data science is about storytelling, and so are product design, sales, and marketing. There are so many intersections in thinking that it just seems natural for us to be involved in all these parts of the business. I might be in the minority, but for me, data science really has nothing to do with #BigData. It has nothing to do with machine learning. It might not have anything to do with statistics. It is about asking questions, developing user empathy, creating an experience, and telling a story. Our medium is data, our medium is code, but the outcome are fantastic product experiences.
We’re always looking for great storytellers: whether data scientists, account managers, or backend engineers. Come join us.