Susan Holcomb on the TDS podcast
Editor’s note: This is the third episode of the Towards Data Science podcast “Climbing the Data Science Ladder” series, hosted by Jeremie Harris, Edouard Harris and Russell Pollari. Together, they run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:
It’s easy to think of data science as a technical discipline, but in practice, things don’t really work out that way. If you’re going to be a successful data scientist, people will need to believe that you can add value in order to hire you, people will need to believe in your pet project in order to endorse it within your company, and people will need to make decisions based on the insights you pull out of your data.
Although it’s easy to forget about the human element, managing it is one of the most useful skills you can develop if you want to climb the data science ladder, and land that first job, or that promotion you’re after. And that’s exactly why we sat down with Susan Holcomb, the former Head of Data at Pebble, the world’s first smartwatch company.
When Pebble first hired her, Susan was fresh out of grad school in physics, and had never led a team, or interacted with startup executives. As the company grew, she had to figure out how to get Pebble’s leadership to support her effort to push the company in a more data-driven direction, at the same time as she managed a team of data scientists for the first time. During our conversation, Susan covers a lot of what she’s learned from the process:
- It’s crucial to think about how well your personality aligns with the roles you’re targeting. If you want to work at an early-stage startup, then great — but don’t expect to be able to tinker with models and optimize AUC scores all day. The earlier the stage of the company, the more you’ll need to take on a generalist role. That often means being equal parts analyst, data scientist, and data engineer.
- Data scientists are problem solvers, and not algorithm generators. Shortly after Susan joined Pebble, she noticed an anomaly in her data: it seemed as though many users were sleeping with their smartwatches on. She took it upon herself to do a poll around the office, and sure enough, it turned out that 10% of people were doing just that. That insight led to a powerful set of features for sleep tracking and health monitoring, that were way ahead of their time. Most of these didn’t use fancy algorithms like neural networks, or even decision trees; they were simple rule-based systems, but they did the trick. The moral of the story: good data scientists don’t over-engineer fancy algorithms — they spot trends in data fast, and use them to solve clear business problems.
- Most people are terrible at reaching out to other people for things like referrals or technical help. They end up messaging people on LinkedIn without offering them value (e.g. “I was wondering if you could introduce me to some people in your network…”), or sending hundreds of self-serving, copy/pasted emails to recruiters when they’re looking for a job. It’s worth taking the time to research the people you reach out to, and to show them that you’ve done just that in your messages. Take a guess at what their problems might be (e.g. “I was looking at your website and noticed that your product recommendations aren’t as specific as they could be…”) and offer a solution (“I have a couple of ideas about ways you could improve that…”) before asking for what you want.
Unfortunately, data scientists often neglect the business and personal realities of the industry, but talking to Susan makes one thing clear: personal growth (and career advancement) in data science become much easier the moment you allow yourself to take a more human view of the field.
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