Meet: Flaminia Catalli

This is RatePay | Reading time - 3 min

Our colleagues are the heart and soul of RatePAY. Therefore we like to introduce you to some of them in a Q&A. Flaminia is a data scientist and is working for the company since more than one and a half years.

Q: In a few sentences, what are you and your team members doing?
A: Our main goal is to prevent fraud and our major resource is data. We basically let data of recorded transactions show us its patterns. We want to learn how fraudsters try to hack our system to be able to counteract their attacks. Based on data, we develop and use algorithms. They are mainly machine learning based methodologies.

Q: Are there any obstacles for you to do that?
A: Actually, I do not see any obstacle, it´s more of a challenge. We always push the models so they perform better. For example, we work a lot on the input variables of our models by engineering them so that the model can differentiate more precisely between a good and a bad transaction. Fraudsters consequently change their behaviour, which makes it hard for us. We have to be at the same pace as they are to prevent our merchants and their customers from harm.

Q: Please describe the work inside your team.
A: In general, we are a very mixed team coming from different countries, having different backgrounds and so on. This is our beauty and power. Our cooperation goes very well, we work together on an intense level. Compared to where I worked before, it is very enjoyable and I feel a profound sense of team spirit. We do have a common goal, that we are aiming to reach together.

Flaminia and her colleagues

Q: How did your interest in data science develop?
A: Well, I have to reach back a bit to answer that. I studied Physics in Rome and specialized myself in Geophysics later in Bologna, during my PhD school. After my studies I worked as researcher in the field of statistical seismology. Data, statistics and modelling were already my daily bread and butter. Then my husband got an offering from the Technical University in Berlin. So, we moved to the city and I thought, it would be easy for me to find a job. But it turned out to be quite hard indeed. After a few months, I finally found a job at the ETH in Zurich. I commuted for over two years, while our daughter stayed with my husband in Berlin. In the meanwhile, I had another baby and my contract in Zurich ended. During my maternity leave I wrote a research project that have been granted from the Deutsche Forschungsgemeinschaft. I worked then two years in Postdam. But in the academy there is no security in the long run, it’s very difficult to make a solid career and it can be easily very frustrating. Eventually, I decided to look for alternatives and found out about data science.

Q: Moving into a completely new field can be hard – how did you prepare yourself?
A: I came across the field while researching onIine, so I started with online-courses and going to different meet-ups. In Berlin, there is a big interest and a growing community on the topic, so there are a lot of possibilities to learn. Before I came to RatePAY, it was basically all self-education. But coming into the company at the beginning was like flying in from the moon.

Q: Why is that?
A: Coming from a so different field, I have never heard of things like balance sheets and desagio or agile working frameworks. I had to learn about different new topics and adapt myself quickly. During meetings and stand-ups, all the German acronyms were quite hard to understand. Even though, it also had positive aspects. “Coming from the moon” meant my perspective was unaffected. And my colleagues and the team leader really made a huge effort to help me understanding and getting ready for the job. I really feel to be grown up hand in hand with my team and this company. They gave me an opportunity and I made the most of it.

Q: For anyone interested in doing data science, what is a requirement?
A: Curiosity and versatility. You will do something new basically all the time. Therefore, you have to keep learning. To me, this is perfect, because it meets my needs. I could not imagine doing a job, which does not envisage to keep learning constantly.

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