AI & ML Science Career Skills
A skills lens on career development
AI and ML roles are relatively new in the tech world, and many new ML scientists find themselves in roles and companies without experienced colleagues to learn from. Add to that the ill-defined role and overlap with other tech jobs, it can be hard to carve out a career plan and figure out how exactly to progress as an AI Scientist.
One lens through which to look at career growth is skills, which broadly fall into two categories:
- Functional skills, or technical skills, that you need to have in order to do your job — like coding, data visualisation, ML experimentation
- Leadership skills, or soft skills, that help you make more impact in your role — like public speaking, communication, and strategic thinking
Career growth is often about growth in leadership skills. Yes, you gain more functional skills throughout your career. You get faster at coding, can develop an intuition for tuning hyperparameters, and can create better graphs. But, the number of hours in the day doesn’t change, and there’s only so much work one person — even one very experienced and capable person — can do. The real growth in impact comes from leadership.
Step 1 — evaluate the present
A good place to start is to evaluate your current skills and strengths. For this, unless you’re very self-aware and perceptive, you’ll need input from others! Ask colleagues and friends who know you well. Look at your performance reviews, and feedback you’ve got in the past from people you’ve worked with.
It can be surprising to learn that the behaviours you take for granted because they’re second nature are skills that your colleagues really value.
Aside from explicit feedback, online tools like the Values in Action tool can help identify your strengths.
On the flip side, knowing your weaknesses is also an important piece of knowledge to understand how to be most effective. Some weaknesses absolutely need to be addressed as they’re career-limiting. However, learning to leverage your strengths can often be a much more effective path towards career growth than a focus on addressing your weaknesses.
Step 2 — plan for the future
Next, think about the skills you’d like to improve. Note that this list is not necessarily the same as your weaknesses. These will also be different depending on which direction you want to take your career and where you are in that progression.
If you want to move into management, for example, then improving your coding skills might not be a big part of your future. In contrast, project management experience is likely very important. A colleague who is aiming at being a senior scientist might be best focusing on breadth of their ML knowledge. Chatting with your manager and other senior colleagues can be a good way to learn about the skills that particular roles and particular levels of seniority demand.
Step 3 — find the opportunities
The third step is to intentionally look for opportunities to develop those skills. Sometimes, circumstances line up to give you the opportunity to develop exactly the skills you want. Sometimes though, you have to create the opportunity by being vocal about what you want and how you want to go about it.
Thinking in terms of skills can also help you weigh up an opportunity that comes your way. For example, suppose you’re invited to give a talk. The talk requires some preparation and would result in some small amount of visibility in return. If you’re keen to improve public speaking, it might be a great opportunity. But if you’re already confident in public speaking and the time would be best spent elsewhere, you might decline, or pass the opportunity to someone who would benefit from the experience.
It’s inevitable that your current role won’t line up exactly with the skills you want to develop. But now, knowing what opportunities are available, you can weigh up the pros and cons to make an informed decision about whether your current situation provides the right opportunities to develop skills, or whether you ought to be pursuing your career elsewhere.
There are many ways to think about your career, growth and opportunities in AI roles. Skills is just one lens through which to view these choices that can help focus your attention on what’s most important to you.
I work with companies building AI technology. Get in touch to explore how we could work together.
