We couldn’t be more inspired after chatting with her about some misconceptions of the industry, team structure, trends, and general advice for people looking for a career change.
If you’re looking for opportunities in data or AI, read on!
Are there misconceptions about career paths in AI and machine learning?
Yes. People usually think that in order to work in AI, you need to be very tech-savvy and know a programming language, but that’s actually not always the case.
Tech people are needed on AI teams, but we also need people with industry knowledge and who are able to translate theory to practice. Business people can transform data into decisions and insights, and hybrid professionals with both backgrounds can play a crucial role on these teams.
Another big misconception is when people conflate data with AI, but most projects don’t actually involve artificial intelligence — sometimes they’re advanced analysis. It’s important to understand these concepts.
Speaking of “hybrid teams,” why are they so popular?
I’m a big fan of “melted” or “hybrid” teams because the work is fundamentally better when there’s diversity on a team. If you can maintain open, constructive communication, it’s better to work with people who think differently from you.
To deliver a good AI product, a team needs to have people with business (or “red,” as some call it), tech (“blue”), and hybrid (“purple”) profiles.
What are some hybrid roles? Can you give us some examples?
Project managers, subject matter experts, and advisors are some examples.
If you have strong technical skills but are lacking business knowledge (or vice versa), how would you acquire the skills you still don’t have?
First and foremost, I’m a technical person. I acquired most of my soft skills doing volunteer work. It’s a great option because you can put your knowledge to good use while challenging yourself and picking up new skills.
For example, “Quotient social” is a non-profit organization that helps foundations, social enterprises, social associations, and all other non-profit organizations use their data. Most of the volunteers there are newly graduated professionals. If you have technical skills but lack business experience, I would recommend volunteering there.
If you’re looking to obtain technical skills specifically, joining an educational program like Le Wagon is a great option. Joining groups such as Women in Machine Learning and Artificial Intelligence is another strategy. It’s a platform for connecting women in machine learning and data science. I’m one of the founders of the Montreal chapter. We realized that, as women, we didn’t have a big enough presence at data related events and wanted a platform to facilitate networking and collaborating together.
Is there more demand for business or tech people on data teams?
On my team (Omnia, Deloitte), we always have business and industry professionals working together with tech experts. Most of the people on the team have technical backgrounds. Business professionals help us a lot by pointing out trends and they need technical people to make sure the data align with their predictions. This collaboration helps us make informed decisions.
How is your team structured at Deloitte?
It depends on the project. I’m working on a government project right now. We’re a team of 15 with a good balance of developers working on SQL and Python, and some industry and business experts who ensure we are aligned with requirements and regulations.
What is the most exciting thing about working with data for you?
The diversity of data! What I don’t want for my career is to be stuck with only one type of industry, having the same challenges all the time. Before working on this government project, I was working with retail, pharmaceutical, financial, insurance — it’s always changing. I never get bored.
Are there any fields growing faster than others?
One of the trends we’re seeing is growth on the data architecture side of projects. With the amount of data growing, we’re seeing a huge increase in architectural strategy.
What’s your advice for people who are changing careers and looking for positions in AI or data?
First, don’t hesitate to write to people on LinkedIn. We tend to think we’re disturbing people when we do that, but if you’re looking for a career change, look for somebody who has your dream job and send them a message — most of them are available for a cup of coffee!
Second, before looking for a job, try doing exercises to discover your strengths and weaknesses. Make sure you know whether you want a technical or business role — or maybe a hybrid position? You need to know what you’re bringing to the table. If you want to change your career, think about the options.
