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I’ve seen many friends transition from an academic or research role to a corporate role. The most successful ones adjusted to corporate work by shifting their mindset in a few crucial ways.
The worlds of academia and industry are governed by different values. The former prizes scientific innovation and intellectual freedom, and the latter prizes building a successful business that delivers impact and profit. If you’re thinking of taking the leap, here are some tips that might ease the way.
- Speed versus accuracy: In academia, publishing technically accurate work is paramount. For example, if you publish a paper saying algorithm A is superior to algorithm B, you’d better be right! In industry, often there’s no right answer. Should you build a system using algorithm A or B? Or should you tackle project X or Y? Rather than striving for the right answer, it’s frequently better to make a quick decision (especially if you have an opportunity to reverse it later).
- Return on investment (ROI) versus novelty: Academia places a high premium on fresh ideas. Many ideas are publishable at least partly because they’re different from their predecessors. (That said, smart researchers don’t just aim to publish, they aim to make a broader impact.) The corporate world evaluates innovations through the lens of ROI and their contribution to the business.
- Experienced versus junior teams: Universities are used to seeing individuals go from not knowing how to code to publishing groundbreaking research. As a result, corporate managers with an academic background often hire junior teams even when the task at hand calls for established expertise. As you know, I’m a strong believer in learning. While a degree program commonly takes years to complete, many business projects can’t wait for team members to grow into a role. By all means, invest heavily in educating your teams — and also consider when you need to hire experienced people to meet your deadlines.
- Interdisciplinary work versus disciplinary specialization: In academia, you can talk exclusively with other machine learning researchers all day long and, through the discussion, push forward the state of the art. In most companies, outside of research labs, a project may require input from teams focused on machine learning, software engineering, product development, sales/marketing, and other areas. To execute it, you need to understand areas outside your speciality and work productively with the teams responsible for them.
- Top-down versus bottom-up management: In an academic setting, decisions about where to devote attention frequently are made at the individual or research group level. In the corporate world, there’s a greater tendency toward top-down management to make sure that teams are aligned and execute successfully.
The shift in mindset between academia and industry is significant, but knowing the key differences in advance can make it easier to shift appropriately. I’ve enjoyed roles in both domains, and both offer valuable ways to move the world forward.
Keep learning!
Andrew
Source: deeplearning.ai