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Its about anything and everything. I, Steven Hancock started this blog for a variety of reasons. I want to start documenting my life and sharing that with others, whether that's family, friends, strangers or my future self. I also want to start sharing my experiences with others in hopes that others can learn from me. Perhaps I can help someone set up an Ubuntu server, write a Django Web Application, or setup a Phonegap Mobile App.

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Where does a data science team fit in within an organization?

August 31, 2015

Throughout my short career as a data scientist I've bounced around through different departments. Our team has expanded by adding members with diverse backgrounds. We've worked on sales and marketing projects, product performance indicators, and financial metrics. And yet we still haven't found our place within the company. So, I'm going to take a look at the industry and see where it puts data science.


There are various Universities and Colleges throughout North America that offer data science programs. Some of these post-secondary institutions offer their data science courses through departments of business, statistics, computer science, and even special analytics or data science departments. Of a list of 23 institutions offering Master Data Science Programs,

  • 9 are schools of business
  • 5 are IT related (information technology, computer science, or engineering colleges)
  • 4 are colleges of science
  • 2 are statistical departments and
  • 3 institutes focus on just data science or analytics

Academics are having a tough time placing data science withing their academic institutions. Data Science is information technology. It relies on computer science & statistics. It has an endless number of business applications. So any of these colleges listed makes sense.


Google data science jobs and you'll find thousands of jobs with hundreds of job titles. Job titles listed will include Data Analyst, Analytics Officer, Business Analyst, Software Engineer, and many more. These titles come from software companies, financial institutions, governments, and marketing agencies. With such wide variety of jobs available and wide variety of applications, where does data science fit within an organization?

In a white paper, Booz | Allen | Hamilton discuss three Data Science organizational structures: Centralized, Diffused, and Deployed models.

  • In the Centralized Model business units bring their concerns to a centralized set of data science teams who are overseen by a Chief Data Scientist (CDS).
  • At the opposite side of the spectrum, the Diffused Model, has embedded teams reporting directly to the heads of their departments.
  • Where as the Deployed Model takes a hybrid approach, teams are embedded in business units but report directly to the CDS.

All these approaches have their own pros and cons. Without any of them being the definitive choice for where to place a data science team within an organization.

Adam Drake, Chief Data Officer at Skyscanner, has a different take. He comes from organizations without multiple teams of data scientists. In an ideal world that data scientists report to the company's board of directors. He cites neutrality as the main reason for this. If reporting to the board isn't an option, report to the highest ranking executive. Data scientists could report to the head of technology or product, however, this may narrow the focus for the team. And as a worst case scenario Drake identifies reporting to the head of Sales or Marketing. Under these circumstances, the data science team will be disconnected from the product team.

My data science hero, DJ Patil, wrote an article on O'Reilly about building data science teams. He claims that most organizations spread data science skills throughout the company, but few commit to having dedicated data science teams. In order for an data-driven organization to thrive they must democratize data. That is, make data accessible to everyone in the company. When Patil started at LinkedIn, the team that went on to become the "data science" team comprised of 1.5 engineers and 5 analysts who supported everyone from CFO to the product managers. Patil visited other tech giants (Yahoo, eBay, Google, Facebook, etc.) to get their experience with data scientists. They all had data science teams that came up with great insight. Once brought to product managers, the teams were met with a "that's nice, but it's not on our roadmap". So LinkedIn's solution was to make their data science teams full product teams, responsible for designing, implementing, and maintaining products.

Where does a Data Science Team fit in?

The industry is all over the map. I found suggestions that data science teams should have their own department under a Chief Data Scientist, or imbed teams throughout the organization in different departments, or have teams work as a product team, or have the team report directly to the CEO. It seems that their is no consensus. Maybe some organization structures work better in different situations. Maybe there just hasn't been enough data science history to know what solution is best. There is agreement that data scientists should be working with other data scientists. No matter where they are place within the organization, they should be placed their as a team.