I co-founded a company! Meet Problem Forward Data Science

Jeff Leek

I have some exciting news about something I’ve been working on for the last year or so. I started a company! It’s called Problem Forward data science. I’m pumped about this new startup for a lot of reasons.

Problem Forward, Not Solution Backward

We have always had a “problem forward, not solution backward” approach to statistics, machine learning and data here at Simply Stats. This has grown out of the Johns Hopkins Biostatistics philosophy of starting with the public health or medical problem you care about and working back to the statistical models, software, and tools you need to solve it.

This idea is so important to us, it is in the name of the company. When we work with people our first goal is to find out the problems and questions that they genuinely care about, then work backward to figure out how to solve them. We don’t come in with a particular predetermined algorithm or strategy. One of the first questions we ask people isn’t about data at all, it is:

What question do you wish you could answer about your business (ignoring if you have the data or not to answer it yet)?

My favorite example of this is Moneyball. This is one of the classic stories about how the Oakland A’s used data to gain a unique advantage. But one of the key messages about this story that often gets missed is that the data weren’t unique to the A’s! Everyone had the same data, the A’s just started with a problem that they needed to solve. They needed to find a unique way to win games that wasn’t as expensive. Then they moved forward to looking at the data and realized that on base percentage was cheaper than home runs. So the A’s used a “problem forward, not solution backward” approach to data analysis.

Using this approach we have worked with companies with a wide variety of needs. Our main capabilities are in data strategy, data cleaning and research quality database generation, modeling and machine learning, and data views through dashboards, reports, and presentations.

Data Scientist as a Service

There are a huge number of data science platform companies out there. Some of them are producing awesome tools, but as any serious data analyst will tell you we are years from automating real data science. We are only very recently seeing formal definitions of what success of a data analysis even means! So it isn’t surprising when general purpose platforms like IBM Watson struggle with specific problems - the problem isn’t specified clearly enough for a platform to solve it yet..

The reason there are so many platforms is that its easy to sell the “cool” part of the problem - say building an AI to classify images or drive a car. But often the deeper problem is (a) figuring out what you even want to or can say with a set of data set, (b) collecting a set of disorganized data, (c) getting buy in from groups with different motivations and data sets, (d) organizing ugly data from different sources or finding new data you might need, and (e) putting your answers in context. These problems are more like “glue” that comes between each of the platforms. We have a phrase we like to use:

To solve your data problem you need a person, not a platform

So we have set up a “platform” that lets you scale up and down the number team members you have to solve data problems, just like you would scale up and down the number of servers or tools that you use on AWS.

This means if you are an early stage startup we can help you scale data science before you can afford to hire a whole team. Even if you are a non-profit or a small academic group we can scale up or down to suit your needs. And if you are a big company we can provide utility data science for projects with tight deadlines.

Working with friends and building East Baltimore

The thing that gets me most excited about this new adventure is working with my really close friend Jamie. It’s been huge for me to learn about the ins and outs of starting and running a business with someone who has decades of experience in the consulting industry.

It’s also exciting to be able to headquarter the company right in East Baltimore and to work to upskill and develop talent here in a neighborhood I care about.

Like what you hear? Get in touch

If you are looking for data science work we’d love to hear from you! Whether you are an academic, a non-profit, a small startup, or a big business our utility model means we can work with you.

If you are interested in working with us contact us here: