Madalina Ciortan is our Head of Data Science, who recently joined us in the Barcelona office. With nearly two decades of professional experience in software development, architecture, applied data science, and research, she is here to lead all our growing data science teams. Read more to hear about her approach to data science, the vision of our data science team, her leadership approach, and more.
Why did you decide to join Kiwi.com?
I decided to join Kiwi.com because I am passionate about the travel industry, I myself am an addicted traveller. Also, I am very excited about the possibility of leveraging the plethora of Machine Learning algorithms to help support the phases of our business and to make an impact.
What excites you the most about Kiwi.com?
We operate in a dynamic, fast-paced environment suited for innovative people who seek to make a significant impact and contribute. What I also find exciting is the company culture, the openness, and the kindness of my colleagues that have made this a truly enjoyable journey so far. Besides that, my role allows me to stay up-to-date with technology while also taking ownership of the transformative potential of AI within our organisation.
What is your approach to data science?
My approach to data science is to blend practical, down-to-earth approaches with sexy state-of-the-art AI models in order to enhance our business’s value. I am also working on transforming the different data science teams in a unified community and practice based on collaboration, permanently animated by knowledge sharing, brainstorming and learning events. It is essential to create a structure that allows people to know each other, to feel comfortable reaching out for help, brainstorming together and having the possibility to improve, to learn at a pace similar and connected to the current AI revolution.
What is your vision for the team, and how do you communicate it effectively to inspire others?
My vision is to create a team of data scientists with a solid technical background, able to innovate and deliver a wide range of solutions that help our business get a competitive edge on the market. Our data scientists will feel comfortable talking to Product Managers and different business stakeholders, create easy-to-understand presentations that adapt their results to the level of the audience, and propose solutions to various problems ranging from reinforcement learning to computer vision, NLP, or LLMs. I am communicating this vision regularly to my teams but also to different stakeholders. Additionally, I am spearheading a company-wide initiative to enhance data science literacy.
How will you foster a culture of continuous learning and innovation within teams?
The data science practice I created is permanently animated by knowledge sharing, brainstorming, hand-on workshops or paper-reading events. Besides, each data scientist can spend 4 hours per week learning and has a personalised learning roadmap, with weekly study topics and follow-ups. We participate actively in conferences and I also organise hackathons, for example, we just finished an ML Olympics event in Brno.
What do you believe is the biggest ethical challenge in data science today, and how do you address it in your work?
The biggest ethical challenges raised by the AI algorithms today are related to concerns about model fairness (e.g. lack of discrimination), transparency (most models are black boxes), and privacy (e.g. GDPR compliance). We are putting in place a responsible AI-compliant work methodology.
What do you find most exciting about the future of data science, and how do you plan to contribute to its advancements?
The versatility of applying data science to any domain is a game changer for the way our society works and the speed at which new advancements are going to be made. At Kiwi, we are considering all the ways we can improve the customer experience from different angles, and this approach will be transformative for our business and our priorities.
How has your view on being a leader changed over the last few years?
I previously held the belief that leadership was a skill separate from and complementary to the path of individual contributor or expert. However, over time, I’ve come to realise that the best leaders are also the best individual contributors, those unicorns assuming full responsibility for projects, understanding all technical difficulties, and leading by example.