Data Scientist to Product Manager? It’s not a common path, they say

Why data science makes great product managers.

You spent a few years crunching data, analyzing information, going deep on customers’ behavior online, offline, omnichannel, mobile, in store, and every possible intersection of those. You are constantly under pressure, deadlines seem to run fast and in your opposite direction. Your Python and R are full of packages and libraries, and your folders contain plenty of v1, v2_lc, …, v11_final_lc (thanks Google for solving that, btw).
Your shoulder hurts for the many pats you were given for your valuable contribution to the team, you have just won a $20 movie card with free pop-corn or even an Amazon voucher.
You finally realize that, while you have acquired a unique skillset, you have learned how to navigate complexity, and you have trained your thought process, you have never been in control.

You start your research, talk to people, and read a bunch of articles, until you realize that your background is incredibly appropriate for a career you had never thought about before: Product Management.

In fact, Data Science makes great Product Managers.

Let’s see why:

Data Scientists and Product Manager use data to inform their decisions.

Data Scientists’ bread and butter is data; they analyze large quantities of information, synthesize it in a few key points, and suggest decisions based on their findings.

Product Managers are obsessed with shipping features their users love. Users’ needs and feature development are based on surveys, research, tests, data.

Data Scientists and Product Managers present their findings to their stakeholders and seek consensus.

Data Scientists present their findings to their audience; regardless whether they work for internal or external clients, they always have to sell their story to their audience and, guess what, they use data to do so.

Product Managers, by definition, coordinate multiple stakeholders at the same time and won’t be successful unless they are able to convince everyone. Data is PM’s friend.

Data Scientists and Product Managers work cross-functionally.

Data Scientists work with and for a number of teams within the organization; they may supply information to clients, client managers, product managers, finance team, and strategy group.

Product Managers work with designers, engineers, market researchers, finance, and product leaders. Everyone generally knows what everyone else is doing and who is the point of contact for specific topics.

Data Scientists and Product Managers see success as a team achievement.

Data Scientists may do most of their work independently. They are often given a task and they execute on it. Successful Data Scientists, however, need the big picture and this often requires creating relationships with the broad team. No matter what, every achievement will never be the result of their sole work.

Product Managers may have the best intuition or create the best mockup; however, success depends on execution and execution requires the entire team to be involved in the process.

Data Scientists and Product Managers have to prioritize.

Data Scientists are overwhelmed with an infinite amount of data; their analyses can take multiple directions and they can always go one level deeper. Hence they are constantly forced to prioritize.

Product Managers are overwhelmed with options; the user-centric approach always leads to a number of different options and the only way to succeed is to focus on what transforms into the impact they are seeking.

Data Scientists and Product Managers need to know the market they work on.

Data Scientists may know every analysis technique but they always have to explain their findings in the context of the market they work on while keeping their eyes open to spot important details.

Product Managers need to know everything about users, competitors, potential entrants, customer journey, and industry-specific dynamics. They have to put themselves in users’ shoes, while maintaining a fresh and unbiased perspective.

Data Science is a phenomenal school for aspiring Product Managers.

Bring this argument to the table next time someone you are seeking advice from tells you “I don’t know anyone who transitioned from Data Science to Product Management, it’s not a common path”.