Profiles of professional mathematicians

The 15-month ZF Global Trainee Programme is a great opportunity to get to know ZF, one of the biggest technology companies in the automotive sector. As a trainee, I complete four projects in four different locations on topics such as artificial intelligence, e-mobility, safety, digitalisation, connectivity and autonomous driving.

I validate and control pricing algorithms, focusing primarily on complex equity and foreign exchange structured products. I am also responsible for the assessment of model risk, model limitations and the analysis of model assumptions.

As a data scientist I solve challenging machine learning problems in order to help our clients improve operations, meet specific business goals or shape new business ideas. At Zühlke I am lucky to get to work in different industries and use my analytical, statistical, programming and communication skills, so it is a very multisided and exciting job!

As a data scientist at Google, I research and develop methods for measuring and analysing the quality of our products and understanding who our users are. I work closely with software engineers and project managers and help them make decisions based on experimentation and statistical evidence.

As a data analyst at Picnic, I am constantly challenged to find smart and pragmatic solutions to the complex problems that arise in this fast- growing company. Ranging from business analytics and automation solutions to forecasting promotional demand, there is never a dull day at Picnic!

My current job focuses on three main aspects: teaching (including supervision of Bachelor’s and Master’s students), research and academic services, including the co-organisation of research seminars, didactic development and outreach activities. In terms of my research activities, I am particularly interested in problems arising from biological or medical questions and I enjoy interacting with colleagues from the life sciences.

As a Master’s trainee at Mobiliar, I supported both the actuarial team and the data science team with my analytical skills. I quantified the inflation risk for motor liability reserves and participated in an international project on the impact of using machine learning in claims reserves. I also trained an image recognition model for the automation of car claims handling. Now I am back at ETH for my doctoral degree.

I work with mathematics on risk management questions arising from the day-to-day business of financial and insurance companies – in particular using probability and statistics. The beauty of my job is that I get to collaborate with researchers all over the world on challenging mathematical problems that have a practical relevance.

As a professor, my job has two aspects: first, I conduct research in pure mathematics and secondly, I pass on my knowledge by teaching, giving lectures, and talks. In my research, my goals are to use abstract mathematical tools from topology (the study of shapes) to understand problems coming from quantum field theories in mathematical physics. What I enjoy most about my job is to constantly learn new things, whether it is about new developments or different fields in research or also when teaching a new class. Usually, this involves communicating and collaborating with others – I have found that math is very social!

The company I work at focuses on pedestrian flow: we measure how people move in public areas, analyse it and give advice on how to optimise it. My typical day consists of programming (creating analyses of data captured by sensors), coordinating projects with clients, writing reports and completing organisational tasks for the firm.