Want to be a software engineer or data scientist in AI? Here’s tips to get you started

Female software engineer standing beside server racks holding a laptop


Andreea Munteanu, an MLOPS product manager at canonical, reflects on the growing popularity of AI and data science in recent years.

Andreea emphasizes the importance of finding a niche and staying passionate about the field. As a female engineer, she encourages continuous learning, engagement with the AI community, and the development of both technical and communication skills. Munteanu also discusses the increasing demand for data scientists and software engineers and the positive changes in diversity and equality within the industry. She encourages women to have confidence, speak up, and embrace failure as part of their career journey in AI.

Andreea Munteanu is a Product Manager at Canonical, for Machine Learning Operations

Andreea Munteanu is a Product Manager at Canonical, for Machine Learning Operations. She looks after Canonical’s portfolio of tools that make AI/ML initiatives great. She is passionate about machine learning and open source.


It even felt to me a bit strange and complex, but I was excited I could change the world. These days, I think just about everybody has played with AI tools like ChatGPT or Midjourney. That exposure has meant that a lot of companies have gone from thinking that utilising AI technology was out of reach, to realising it is possible for them to start integrating it and see huge benefits. Roles such as machine learning engineers engineers or data scientists are already growing in demand, so there is huge opportunity for budding AI enthusiasts.  At the same time, the popularity that AI got grew the trust that people have, causing a shift from a doubtful technology to an innovative solution that is worth taking seriously.

My own journey began with studying telecoms engineering, and in fact I started to work in a telecoms company when I was still a student. I became very passionate about the fact that telcos generate a huge amount of data – imagine all those antennas, all those systems – but engineers were still looking to optimise network performance rather than using the data at their fingertips. That’s what really sparked my interest in data science. In parallel, I did my bachelor 

In Romania, where I come from, people thought ‘good careers’ meant medicine or law, but I went down the engineering path because I excelled at maths and physics, and found it far more interesting. But I quickly realised the gender imbalance within STEM; when I studied engineering, there were just two females in the whole year of more than 100 people, all males. It felt overwhelming and often uncomfortable. Since then, I have worked in a lot of tech organisations, and it’s definitely the case that you will sometimes be the only female in the room. Fortunately, this is a scenario that happens less and less, as many organisations are succeeding in addressing the gap. 

As a female engineer working in AI, there are many ways you can stay on top of your game, but the most important thing is to find a niche that you can master and enjoy. I would also add here that unlike traditional software development roles, AI benefitted these days from a big popularity amongst women. In some African regions, there is a huge shift, with women being majoritary in the industry.


I am lucky to have always had managers who encouraged me to work towards something I am passionate about and avoid getting stuck in any challenge may arise, but rather focus on the solutions I could find. This is the secret for females in STEM: do things that you really love. I had landed in engineering almost by chance but when I started playing with data, then found myself getting lost in it and becoming fascinated. 

I’d absolutely suggest immersing yourself in different programming languages, technologies and ways of working, until you find that passion. I started on the data science side, but have moved over to working on tools that enable data scientists and machine learning engineers to perform the job better. If you’re not enjoying your job, don’t be afraid to change roles, or change companies.

Mastering fundamental concepts such as algorithms, data structures and problem-solving skills will help to progress your career either as a data scientist or software engineer. Master both programming and mathematics where you can. Data scientists with a background in software can work independently, without requiring as much outside help. Software engineers with data science skills can help deliver tools that work.


Perhaps my most important piece of advice is to never stop finding ways to learn. I am always impressed by how quickly the AI and machine learning world is evolving: new tools, new frameworks, and new use cases seem to pop up almost every day. 

It often feels overwhelming to stay up to date with everything. This is why I always recommend data scientists become ‘super users’ of a few tools and then learn more about new ones when needed. When you need to learn, there is a lot of documentation online, plus online courses such as Coursera and Udemy.  Also, AI landscape benefits from a wide range of open source tools, which includes thriving communities. It eases the process to both learn and get unblocked when the situation requires it.

Being in touch with the wider community through conferences, coding bootcamps and online communities helps you stay up to date with trends, and keep you in touch with new opportunities. Don’t be afraid to go to ‘real world’ events. I’ve embraced public speaking as part of my role, and it gave me a huge confidence boost to visit events. As an attendee, you gain knowledge, you network and you learn from those who have more experience. As a speaker, you get real-time feedback, you engage with your users and contributors and you gain insight. I once heard that being a good developer is someone who can go beyond the coding part, and be able to explain easily what the piece of code does. Communication skills can really help to complement your technical ones.


There’s an enormous skills gap in data science and software engineering world, so once you have developed these skills, you will be in demand. Since data science was defined as the sexiest job of the 21st century just over a decade ago, the job has kept growing and growing, and so have the salaries. Software engineers are also seeing ever-growing demand

The most important part of starting on that ladder is to find your passion, even if that involves changing roles a few times, and to keep learning, every day, to ensure your skillset stays up to date. 

There’s never been a better time to train as a data scientist or software engineer. In terms of diversity and equality, the situation is getting better every year. In my team, which is a highly technical team, we now have an equal number of males and females. There is also a growing community of women in STEM who can offer support. My advice for other women would be to always have the confidence to speak up, and don’t be afraid of failure. Failure is part of the career path for everyone. As a field, AI can seem intimidating, but once you find that passion, there will be no stopping you.


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