Written by Marie-Amélie Masnou 

In our October webinar, we welcomed Caroline Williams, a founder of The Do Good Only Company, a social enterprise focused on digital inclusion, based in the Netherlands. Their commitment to social justice is anchored in their work to creating an inclusive IT, Data and AI workforce today.

Caroline was talking on the topic “Data scientist is not just a role per se, it is a set of skills”

“While I was at Microsoft Netherlands, I started and taught the first Azure Academy for Women. After all of our participants completed the program, I was hooked on expanding the idea and started my company in July 2018 to ensure that these kinds of programs didn’t disappear. Two years later and the fourth cohort of the SkillsUP Lab has begun.”, Caroline says.

The fact is that in their program, they don’t produce data scientists, rather they teach people practical data science skills, that they can apply to any role. And the program is open to everyone.

“Job experience is critical to a successful transition. At the end of their four-month (initial) training period, our Data Professionals start their paid traineeships with companies as their next career step towards permanent employment. The inclusive data community that we are building is multigenerational, multicultural and multidisciplinary.”, Caroline adds.

Indeed, talking about data and how it can change lives is Caroline’s favorite topic. She has already run 5 cohorts in which she and her team are teaching people practical data science and analytics and getting them into the job market to make the world of data and IT far more inclusive than it is.

“If we want to have a more inclusive IT world, then it begins with the people we have around us today. Women and people from underrepresented communities are often left out so it is our purpose to break the walls.”, says Caroline.

She is a self-taught techie. After 3 years in IT as a data scientist, she had enough of what was around her and pretty much like all women around her, she felt ‘done’! Needed to refill batteries with inspirational work.

The fact is that the percentage of women in the tech footprint is not expanding. And this is because 51% of women leave tech after 10 years and don’t return!

So Caroline went on asking herself key questions: Which skills are the most transferable? Which are bringing the most opportunities?

And these are actually the data skills.

As Caroline points out, “If you learn a programming language, you are somehow trapped in software development and this is a limited funnel. So if you want more inclusion, you need to be able to fill in all the way across teams, not in a limited function or capacity. Then data science was the obvious choice and is in scarcity anyway.”

In Caroline’s SkillsUp Lab, people are not called data scientists. They are Data Professionals because while everyone learns analytics, cleaning, visualization, building models and programming, they might have their preferences.

And a good healthy data culture has people who specialize in all those different areas. Some love nothing better than making data usable and able to visualize them. They need to know how models work, cleaning and so on, even that might not be their affinity.

You can silo everyone because you believe this is driving efficiency, it is easy to track but you are missing out on the ‘mess’ where innovation happens and problems are solved. So if you do pairing and remove the perception that everybody wants to work 40 hours or more per week, then you gain the most because people working in your organization are bringing their full self and they love what they do and they feel that what they are doing is contributing.

“If you want a more inclusive IT, then you need to look at people differently and bring those skills together. No one is a unicorn.“, she adds. “If people are not stimulated, they will leave their roles. So open up those spaces, look at job sharing to have people synergize.”

Caroline’s cohort goes into traineeship after acquiring the Data Professionals certification and she makes sure they go to places where they can thrive.

The 16 weeks program to graduation includes job coaching followed by a 6 months Traineeship.

Caroline added that “the power skills make the difference in the training. It is not just technical skills. And the graduates can be themselves and thrive in any company. They are role models, holding the door open for somebody behind them. It is up to the trainees to walk thru the door and put the effort into it. You commit to it and take control. Your resilience has to be strong if you work in IT.”

Don’t be afraid to know and put a value on the skills that you have. If your knowledge is in scarcity and the knowledge that you bring also is, make sure to know where your power lies.

Article courtesy of Mısra Turp.

I have been interviewing people from all around the world on my podcast So you want to be a data scientist?. My guests have been from very different positions who all have either the title data scientist or similar. Inspired by everything I’ve heard from my guests on the podcasts, I prepared a list of possible positions you can consider when planning your future.

It’s important to know which one you want to work at, in order to get the most satisfaction out of your job. I understand this is not very easy to do when you have little to no experience in a field. In data science, you can end up in many different types of positions. They all have varying responsibilities, different careers and busyness. But if you are not in the type of position you enjoy, you might end up unhappy.

I didn’t think about this much when I first started. For me, it was a trial and error approach. I have started in a big company as a consultant, only to realise that wasn’t for me. In time I realised an in-house position would suit me better. That’s why I went for a change and I will be starting in my new position next week. I’m happy and excited to experience how being an in-house data scientist will fit me.

If you don’t want to spend a couple of years of your life trying and erring as I did, here is a list of common types of positions for data scientists. You can find the link to the episode where I interview the guest with that position below the titles.