Education

2020-2022 (expected)

Msc, Computer Science; KTH Royal Institute of Technology, Stockholm

• GPA: 4.7/5

2017-2020

BSc, Engineering Physics; KTH Royal Institute of Technology, Stockholm

• GPA: 4.7/5

2019

Exchange semester; Nanyang Technological University, Singapore

Experience

Gjensidige Forsikring

Gjensidige is a leading Nordic insurance group listed on the Oslo Stock Exchange.

• Developed a machine learning based pricing model that on average was 20% better at predicting the risk of a customer compared to the current model employed by Gjensidige

• Currently working on a binary classifier to predict whether an existing customer is likely to buy new insurance products to boost additional sales

• Built web scraping applications to collect large amounts of data to aid the CRM team

• Create reports using SQL/SAS on a monthly basis, and communicate results across different departments of Gjensidige

Formula Student

One of the largest engineering competitions in the world, where university teams design, build and compete with small scale race cars.

• Developed and implemented path planning algorithms for the driverless system of the car

• Worked together with 10+ other members in a startup-like environment

• Collaborated with academic experts in the field to ensure our line of attack was feasible

• Qualified to the most reputable race at Hockenheimring in Germany

Projects

Tree-based Machine Learning for Insurance Pricing

As part of my bachelor thesis at Gjensidige I developed, tested and compared tree-based machine learning models on Gjensidige’s home insurance data set

• Performed feature engineering and cleaning of large amounts of data
• Had to deal with very unbalanced data when training the models
• Evaluated the models’ predictive performance, as well as potential profitability

For more info see: link

Developing a Camera-based Method for Measuring Blood Pressure

Researched the possibility of using a video camera and image processing in Matlab to measure blood pressure non-invasively. The project was carried out at KTH School of Technology and Health.

Report available here

Programming Languages

Python: I have worked extensively with Python both professionally and in academic contexts. I have experience using Python for data science through Numpy, Pandas, sklearn and seaborn, but also for larger software projects.

SQL/SAS: SQL and SAS is something I use almost everyday in my work at Gjensidige for querying from our large data tables. I am confident with writing complex queries and used to generate reports through SQL and SAS.

R: R is another language I have used a lot in my work, I have built machine learning models, performed analysis of large sets of data and developed libraries of functions, among other things.

I also have knowledge of C and Java through course work

Languages

• Swedish (native speaker)
• English (Fluent)
• Mandarin Chinese (A2 proficiency)