Today, we are interviewing someone from my  country and a pioneer of data science education in Italy: Paola Cerchiello, Senior Assistant Professor of Data Science at University of Pavia.

The University of Pavia is one of the oldest colleges in the world, being founded in 1361. It offers 84 different types of bachelor degrees, 29 masters and 22 PhDs. Paola belongs to the Department of Statistics and teaches courses in Data Science (Applied Statistics, Big Data Analysis, Data Mining) for a number of degrees.


Dr. Paola Cerchiello

What Contributed to Paola’s Success?

We asked Paola to tell us the things that contributed most to her success. She conveyed three basic themes that characterized her professional career:

  • Theme #1: FLEXIBILITY
  • Theme #2: CONTINUOUS EDUCATION
  • Theme #3: DON’T LIMIT YOURSELF

Paola did not start her academic career in Statistics. She was a student of business administration at University of Pavia. Her Professor of Statistics, Paolo Giudici, who remains her boss and mentor, pushed her to add STEM subjects in her academic curricula. This eventually led her to sign up and graduate with a PhD in Statistics from the University of Milano-Bicocca in 2004. While a PhD student, she spent a period at Rutgers University. While studying at Rutgers she fell in love with textual analysis, something not yet popular among statisticians in Italy and Europe.

It the beginning it was not easy to have her research understood and published: she was at the intersection of statistics, finance, and computer science. And traditional journals typically prefer to publish papers, which focus on specific fields. At some point in the mid-2000s, mainstream financial journals start publishing work around textual analysis and its application to finance (we cover some here). Finally, she felt in the right place at the right time. Her flexibility towards multi-disciplinary approaches was paramount.

Paola’s Next Frontier

All those years of studying finally paid off and Paula is happy being one of the few females studying these topics in Europe. Requests to participate at seminars and to teach classes around Europe are abundant! For instance, she collaborates with the European Central Bank, Bank of Italy, and the Bundesbank.

As far as teaching activity, Paula started with courses in Applied Statistics but now teaches Big Data Analysis and Data Science. She teaches courses at the University of Pavia as well as the newly formed Master in Data Science for Complex Economic Systems at the University of Torino-Collegio Carlo Alberto and Master in Fintech & Innovation at Lumsa University in Rome.

As far as research goes, her research group was recently awarded a 2.5 million euro grant to coordinate the Horizon 2020 FinTech research project. This project will see 25 other different European universities and researchers working together. The knowledge produced by this interdisciplinary project will be shared with national and supranational regulators as well as the fintech sector.

One thing is clear in Paola’s career: never stop studying and learning new things. And what is more important, never stop sharing your knowledge with students. Yes, it requires effort to update your syllabus and power point presentations for every new semester, but it is the best way to make the world progress!

Closing Thoughts from Paola

Back to some personal tips for our female audience who want to pursue a quantitative finance career. First of all, some of the nascent fields around finance like data science are so niche that there are a lot of opportunities and being a female is seen as a privilege, according to Paola’s experience. This goes without saying, but Paula also recommends studying quantitative subjects, such as coding, math, and statistics. A knowledge base is key if one wants to be respected in the quantitative community.

One important motto that helped Paola is to never limit herself to certain subjects or experiences. She thinks that part of the issue of the under-representation of women in finance is due to a sort of adverse selection bias: culturally women think they are more prepared for humanistic subjects like sociology, psychology, nursing, and so forth. Paula never questioned whether she was a fit to be a statistician. And she credits much of this mindset thanks to a special experience during college: she lived in a female-only “merit-based” campus housing program — ‘Almo Collegio Nuovo’ — in Pavia'(the Italian version of a sorority). Here she realized that her housemates (about 100) were tackling very diverse subjects: from physics to engineering, medicine, sociology etc. In this multidisciplinary and stimulating environment she developed awareness on the possibility for women to achieve any goal without limits.

Paula’s advice is in line with a recent study by McKinsey, which surveyed more than 14,000 employees at 39 financial services companies as well as interviewed 12 female senior executives. The results are staggering: while female representation at entry-level positions is at par with men, only 19% make it to the C-suite. Among some of the possible reasons behind the under-representation at top levels is an ambition and confidence gap. Not having many female role models to look up to may increase the likelihood that young women deselect themselves from these top positions.

We hope that the #womeninfinanceknowstuff is a little step toward increasing the representation of women in finance at all levels.

Thank you, Paola: it has been a great pleasure and we wish you continued success!


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