The quant toolbox
Lovisa Thordin, another Nordea Markets representative at the Quant Fair, told students that she didn’t know what she wanted to work with when she was studying Engineering Mathematics at Lund. She reached out to Nordea when she embarked on her Master’s thesis, and later landed a quantitative/data analyst role in Nordea Markets.
“The job is a great mix between math and business. I appreciate being close to real-life problems and using the skills I learned at university,” she said.
The presenters outlined three basic career paths: quant analyst, quant trader and quant developer.
Malthe Kirkbro, another Nordea presenter with a background in software development, provided the quant developer perspective. He described his work taking models and putting them into production as very “result based.”
“You can immediately see the fruits of your labour in the P&L (profit and loss). I like working with these close-to-the-metal computer science problems that come down to the microsecond. It’s one of the coolest places to work in tech these days,” he said.
Getting your hands dirty with machine learning
To bring theory to life, the Nordea Markets quants presented two real-world examples from their work. Malthe described the use of modelling to figure out the current market prices for a given currency pair in the FX (foreign exchange) market, while Lars gave an example of using machine learning and “clustering” to figure out which customers are a good match for a new Nordea automation product.
“There are many ways to get your hands dirty with machine learning,” Lars said.
Amanda Ramirez, a Lund student who co-organised the workshop, says she was excited to see machine learning concepts applied in practice.
“Machine learning is a hot topic you read about in a lot of research papers. It was great to see that it can be used to make actual business decisions,” she says.
Data science and machine learning are trends that aren’t going away anytime soon, according to Daniel Schiermer. He notes there are many examples where they can be key enablers, for example, with the automation of manual processes or making daily work more efficient by processing and enriching vast amounts of data, adding:
“Data is opening up a lot of potential for us, and we need quants to unleash that potential.”