I’ve written before about analytics teams as a crucial function in today’s technology companies. Technology companies are rapidly hiring analyst roles to pair with their product teams. And while my previous post discussed how to hire analysts and structure their teams within organizations, I haven’t written about how analysts should approach their careers.

Many technology roles, at startups in particular, have an issue with career progression. While established industries have defined career ladders, the path of career advancement is much less clear in many technology roles. Engineering, being the largest and oldest function in technology companies, now has a well defined individual contributor and manager career path all the way up to VP Engineering and CTO. Product Managers know they can progress to manager roles at their companies all the way to VP Product, and if they want to remain and individual contributor, they can still grow by working on more and more complex and strategic products over time. As I’ve talked to many analysts and analytics teams, this progression is not as well defined. I will outline how I think about this progression as someone who has been an analyst and managed analysts.

Option 1: Graduate into Data Science

If someone wants to remain an individual contributor and not manage, at some point the only way to become a better analyst is to graduate into a data science role. Now, there is some confusion with where the line is between analyst and data scientist, and many companies just call all of their analysts data science as a form of title inflation. I define the role of an analyst as someone who uses data to help identify and communicate business opportunities, and drive decisions for teams. This includes targeted analysis driven by others as well as free form analysis driven the analyst. From a process perspective, this includes everything from making recommendations, helping with experimentation, and creating dashboards to help others make decisions. From a tooling perspective, this means everything from writing SQL queries, identifying logging opportunities for product engineering and database design opportunities with data engineering, creating new dashboards and visualizations. An analyst retrieves, analyzes, and recommends, and is judged by not only how good those recommendations are, but how often they are followed.

So, how does data science differ? A data scientist writes code beyond SQL to manipulate data for analysis and potentially for product experience. A data scientist can write an algorithm that powers a personalized experience in the product, or just do more complicated analyses requiring more sophisticated querying using Python, R, etc. Data scientists jump in when analyses are too complicated to be handled by analysts, and also frequently partner or embed with product and engineering teams to change the product. This is more than just a higher-power analyst role though. Data scientists have deep expertise in certain areas, like machine learning, statistical inference, and focus on solving specific, hard problems over longer time horizons.

Option 2: Become an Analytics Manager
If you wish to get on a management track, becoming an analytics manager is the natural path. Since analysts are being hired so frequently, they need managers who can mentor and coordinate learnings between teams. While analysts are best embedded, analytics management bears the important responsibility of solving company-wide analytics issues related to tooling, process, etc.

Option 3: Graduate into Product Management
The third path that analysts can choose to grow their career is migrate into product management. Technically, product managers and analysts are peers in cross-functional teams, but product management has better career pathing that doesn’t require as much technical investment as data science, and product managers tend to have a bit more power in organizations today.

The migration of analysts to product manager is increasingly common as more and more product teams rely on data as the foundation for most decision-making. This has certainly been most true on growth teams and teams that utilize personalization, but I believe all future product teams are data savvy. A significant percentage of product managers at Pinterest started as analysts at the company. This same migration is also true for marketing analysts. They tend to become quantitative marketers over time, or switch to product analytics.

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Being successful as an analyst is peculiar is that it almost requires a switch in roles over the time in ways that are not true for design, engineering, and many other roles in technology companies. Fortunately, the analyst has a lot of choices on how to progress within an organization. Hopefully, managers of analysts get better at outlining these different opportunities and help analysts position themselves toward the best ones for them over time.

Thanks to George Xing for reading early drafts of this.

Currently listening to Compro by Skee Mask.