Finding a job has a few predictable steps that include selecting the companies to which you want to apply, preparing for interviews, and finally picking a role and negotiating a salary and benefits. In this chapter, I’d like to focus on a framework that’s useful for many job seekers in AI, especially those who are entering AI from a different field.
If you’re considering your next job, ask yourself:
- Are you switching roles? For example, if you’re a software engineer, university student, or physicist who’s looking to become a machine learning engineer, that’s a role switch.
- Are you switching industries? For example, if you work for a healthcare company, financial services company, or a government agency and want to work for a software company, that’s a switch in industries.
A product manager at a tech startup who becomes a data scientist at the same company (or a different one) has switched roles. A marketer at a manufacturing firm who becomes a marketer in a tech company has switched industries. An analyst in a financial services company who becomes a machine learning engineer in a tech company has switched both roles and industries.
If you’re looking for your first job in AI, you’ll probably find switching either roles or industries easier than doing both at the same time. Let’s say you’re the analyst working in financial services:
- If you find a data science or machine learning job in financial services, you can continue to use your domain-specific knowledge while gaining knowledge and expertise in AI. After working in this role for a while, you’ll be better positioned to switch to a tech company (if that’s still your goal).
- Alternatively, if you become an analyst in a tech company, you can continue to use your skills as an analyst but apply them to a different industry. Being part of a tech company also makes it much easier to learn from colleagues about practical challenges of AI, key skills to be successful in AI, and so on.
If you’re considering a role switch, a startup can be an easier place to do it than a big company. While there are exceptions, startups usually don’t have enough people to do all the desired work. If you’re able to help with AI tasks — even if it’s not your official job — your work is likely to be appreciated. This lays the groundwork for a possible role switch without needing to leave the company. In contrast, in a big company, a rigid reward system is more likely to reward you for doing your job well (and your manager for supporting you in doing the job for which you were hired), but it’s not as likely to reward contributions outside your job’s scope.
After working for a while in your desired role and industry (for example, a machine learning engineer in a tech company), you’ll have a good sense of the requirements for that role in that industry at a more senior level. You’ll also have a network within that industry to help you along. So future job searches — if you choose to stick with the role and industry — likely will be easier.
When changing jobs, you’re taking a step into the unknown, particularly if you’re switching either roles or industries. One of the most underused tools for becoming more familiar with a new role and/or industry is the informational interview. I’ll share more about that in the next chapter.
I’m grateful to Salwa Nur Muhammad, CEO of FourthBrain (a DeepLearning.AI affiliate), for providing some of the ideas presented in this chapter.
Overcoming Uncertainty
There’s a lot we don’t know about the future: When will we cure Alzheimer’s disease? Who will win the next election? Or, in a business context, how many customers will we have next year?
With so many changes going on in the world, many people are feeling stressed about the future, especially when it comes to finding a job. I have a practice that helps me regain a sense of control. Faced with uncertainty, I try to:
- Make a list of plausible scenarios, acknowledging that I don’t know which will come to pass.
- Create a plan of action for each scenario.
- Start executing actions that seem reasonable.
- Review scenarios and plans periodically as the future comes into focus.
For example, during the Covid-19 pandemic back in March 2020, I did this scenario planning exercise. I imagined quick (three months), medium (one year), and slow (two years) recoveries from Covid-19 and made plans for managing each case. These plans have helped me prioritize where I can.
The same method can apply to personal life, too. If you’re not sure you’ll pass an exam, get a job offer, or be granted a visa — all of which can be stressful — you can write out what you’d do in each of the likely scenarios. Thinking through the possibilities and following through on plans can help you navigate the future effectively no matter what it brings.
Bonus: With training in AI and statistics, you can calculate a probability for each scenario. I’m a fan of the Superforecasting methodology, in which the judgments of many experts are synthesized into a probability estimate.