Community Spotlight: Anastasiia Kulakova

How data for good projects can shape a data scientist

By Ioanna Lykiardopoulou

Every month, we spotlight one member for our community. At KickstartAI, we believe that meaningful progress starts with individuals — builders, researchers, founders, and practitioners — who are applying AI in bold, practical ways. Our Community Spotlight series celebrates their work, their challenges, and the lessons they’re learning along the way.

This month, we’re featuring Anastasiia Kulakova, Data Scientist II at Kraken and volunteer at CorrelAid. From applying data science in the energy sector to contributing to data for good projects in the nonprofit world, this is her story.

What drew Anastasiia to data science was that, at its core, lies an optimization problem. “I think the concept of achieving more with less has always excited me,” she says. “I’m very much interested in end-to-end data science projects that result in real-life outcomes.” 

This outlook guides both her professional and personal life. At Kraken, Anastasiia uses applied data science for the sustainable energy sector. Outside of work, she’s an active supporter of the Data4Good movement — a wide initiative that applies data science for societal benefit rather than profit, from tackling climate change to supporting human rights among other causes. 

Anastasiia is a volunteer at CorrelAid, a non-profit network of data scientists who aim to advance social good. Volunteering has long been a big part of her life, she says, and she first started contributing to different projects during her university studies. 

It was when she attended a CorrelAid meetup in 2022 that she realized volunteering didn’t necessarily mean offering hours or manual labor. “You could also offer the skills you developed through your education and work training. I thought it was a perfect match!” she recalls. 

Matching refugees with welcome groups 

Anastasiia’s first project with CorrelAid was carried out for the Justice & Peace non-profit organization in the Hague. The goal was to match people with refugee backgrounds to welcome groups: long-term residents or Dutch citizens who could help newcomers navigate language barriers, institutions, and bureaucracy.

To support this, the CorrelAid team built a proper database, where applications from welcome groups and newcomers could be stored and later used for algorithmic matching. “Good matches” could be defined in many ways, but geographical proximity was a clear factor, Anastasiia explains. 

“I contributed to the foundational part of data engineering, setting up the infrastructure,” she explains. “The nonprofit had previously relied on surveys and manual input in different formats, with no existing databases.” 

Battling homelessness 

Another project, which Anastasiia participated in, focused on helping people who were economically homeless — those unable to afford housing — by placing them in spare hotel rooms. 

Hotels could offer vacancies free of charge, giving people a temporary place to stay, sometimes just long enough to prepare for a job interview or take first steps toward employment.

The project was less about advanced data science and more about data management and storytelling, Anastasiia explains, to support the goals of CorrelAid’s partner organization. 

“The first goal was operational efficiency: much of their work was done manually, and without a single source of ‘truth’ they couldn’t effectively identify whether hotel rooms were available or not.” The second goal, she adds, was to raise funding. “They wanted to communicate the project’s positive impact in numbers and create a compelling visualization for donors.”

How data for good projects can change a data scientist’s perspective  

“Working on data for good projects, especially as part of a nonprofit, has made me think more end-to-end in my approach,” Anastasiia notes, “seeing how my work connects to teams, end products, and the wider context.” 

She adds that with nonprofits usually being financially strained, their people wear many hats, are multidisciplinary and multifunctional. “You gradually learn the same type of attitude. It’s likely that in your day job, you’re tied to a particular function or specialization — but in a nonprofit you learn how to be a generalist. We often hear volunteers say that the experience gave them the confidence to take more initiative at work and step outside their comfort zones.” 

Volunteering at CorrelAid has also shifted how Anastasiia thinks about budgets. “As techies, we love shiny new things, but now I try to measure cost vs. impact, even in the commercial setting of my company, where funding is less of a problem.” She’s also more cautious in how she communicates at work, making sure to back her claims with data — a habit shaped by the transparency demands of nonprofit projects.

One of Anastasiia’s dreams is to make data for good volunteering more mainstream. The hurdle she sees is that, often, such projects seem less exciting from a technical perspective. 

“A lot of us are technology geeks. We like to play with LLMs. We like to play with fast computes and new dashboards. But nonprofits are usually less technologically mature organizations, which may need help with setting up the data infrastructure, or creating their first database.” 

But even these small things, she notes, can have a big impact. “Even though we are technical people, we still want to feel the impact of what we’re doing. In bigger organizations sometimes it takes time to deliver this impact, or it’s broken down into many roles and departments, so it seems small,” she says. But in nonprofits, it’s possible to see results very quickly. “There is nothing, and then there is something!” 

Advice to data scientists 

Anastasiia’s advice to data scientists is to learn by doing. “Start with a pet project, set a milestone you want to achieve, but don’t aim for perfection immediately.”

A common challenge, she says, is falling too deeply into knowledge gaps. The key is deciding “what’s good enough” to move forward and incrementally make improvements and build features. 

She also encourages participation in communities and meetups. “They are essential for seeing what other tools are there. Maybe you come across one that’s interesting, or that sounds relevant to your problem. You pick it up, you try to work with it, and once you’re comfortable with it, it ends up in your toolbox.” 

💡 Want to learn more about the KickstartAI community and our mission to accelerate AI adoption in the Netherlands? Check out our website, listen to our podcast, or follow us on LinkedIn! Interested in speaking with Anastasiia? Connect with her on LinkdeIn or listen to her podcast!