Data Science

Working as a data scientist in a corporate

Thanks to my work in universities and corporate training, I often get to chat with young people interested in working as data scientists. I decided to put together a couple of articles concerning my experience in both corporates and startups. Hopefully this could be useful to someone.

First of all, what do you mean by “Corporate”?

There is no clear-cut, universally accepted definition. But let’s say that by “corporate” I mean a company with roughly 100+ employees. This could be a private or public company, or a government entity. If the company is big enough, there can be different business units, each of them with their own data science team, or one centralized “Center of Excellence” for all things data science in the organization. Examples include a retail bank, a telecommunications company or the consulting / professional services firms.

What does a data scientist do there?

The role of the data scientist is, in its most common form, business support. Data Science is used as a means to optimize day-to-day operations. While there might be some space for innovation or research and development, prototype development is more or less non-existent. This tends to be outsourced to larger consulting companies, sometimes because they have more senior experts and exposure to wider projects/industries, but the most likely reason is scapegoating. If one is not sure about what to do or where to focus, going to a consulting company is a good way to get wide exposure without committing to anything. Consulting companies are the running sushi at the beginning on one’s career: try a little bit of everything.

Other roles in a large company include being a subject-matter expert that helps end customers, who are typically non-technical. This is more or less an internal consulting role or sometimes, in the case of technology vendors, it can be something more like a sales engineer.

What to expect from working on a large company?

The name of the game is processes. A large company does not care about money, or making people wait. It is all about following structured processes to avoid errors. This can be annoying at one’s early career stages, when it feels like there is so much to do that there is no time to waste.

Another feature to expect is a structured chain of command. This comes in package with the processes, there is a long line of bosses and approvals for everything.

Technology stack is usually already defined in a large company. This is especially true if there is already some data analytics or data science capability. Having to compete nowadays with startups, many companies are discarding expensive proprietary tools in favor or open source tools, favored by startups.

Finally, while there might be lots of data to analyze, this will be very often in silos, scattered across the organization. This is often due to politics, and it can come to genuinely stupid cases. At one project I had to scrape data from an internal website maintained by another department, just because the teams would not cooperate with each other.

What types of models/tasks will I do?

Since a data scientist will mainly support core business operations, models will revolve around the following:

  • Churn, CRM / customer analytics, pricing.
    • Preference for interpretable models, as they need to be used by business decision-makers.
  • Ad-hoc analysis.
    • Employee turnover, productivity.
  • Sometimes, “AI” prototypes.
    • Most likely, not deep learning, but you can get lucky.

Sounds like the job could be boring…

It can be. But can be avoided. The secret is to focus on an industry or problem that interests you. This will keep you engaged, regardless of the politics, processes and all other nonsense typical of large organizations. A good team, and particularly a good mentor, can help.

How does a good mentor/team look like?

First and foremost, it should be someone smart & kind. There is no place in the modern business world for arrogant assholes. There has never been a solid justification for them to exist in the first place, and there is no need to keep them. In particular, your boss should be a nice person to be around. This is hard to describe objectively, as it is a direct function of your own personality and experiences. I would also insist that your first mentors/bosses would be tech savvy. Beware of non-tech direct managers! They can be super dangerous since they have very often acute cases of Dunning-Kruger.

It is also nice for a team to have a diverse skillset and seniority. Teams with smart boys that look more like a college dorm than a company get boring pretty quickly.

How can I find if I am joining a good team?

As a data scientist, you are basically researcher, so research! Go to their social media, internet search, Glassdoor reviews. Ask around in communities like reddit. If you look them up on LinkedIn, look at the average tenure (how long people stay in the company), look at the individual team members and see where they are coming from (background, previous experience, etc). Last but not least, don’t be afraid to ask someone out for a coffee or lunch to ask them how it is to work at that company.

Are there any advantages of working on a corporate?

  • You will learn on one consistent stack, with consistent procedures, and, if you do your research well, with a good mentor and team.
  • Stable job, good to collect brand names in your resume early on that can be leveraged later.
  • Compensation ok, but at this stage being a good apprentice is better.

Any disadvantages of working on a corporate?

  • Everything might move slowly.
    • Expect to wait 2-3 weeks even for small requests.
  • Your job is 100% focused on improving business needs, whether you like it or not.