Machine Learning Job Interviews in Finance: 5 Tips

If you want to work for hedge fund Brevan Howard’s machine learning team, you can expect to encounter Sebastien Guglietta, Brevan’s co-head of “computational intelligence systematic strategies.” Guglietta joined Brevan from Comac Capital, where he spent 18 years as a prop-trading portfolio manager and senior strategist. These days, he’s all about applying “computational intelligence” (i.e., machine learning) to systematic trading, running a team in London that appears to comprise himself and at least another three people.

Guglietta clearly doesn’t hire much, but speaking at last week’s AI and Data Science in Trading conference in London, he gave a few pointers on what to say if you ever find yourself in front of him. In turn, these tips can apply to anyone interviewing for a machine-learning gig in finance IT, whether for a hedge fund, bank, or some other institution.

Don’t Praise Machine-Learning Simplicity 

When you develop a model, you’re reducing a system’s apparent complexity and fighting against its inherent uncertainty, Guglietta said. But a shallow model trying to simulate a macro-economic system with a “deep causal structure” isn’t going to work. There are huge “interdependencies” in an economy, he added: each one has 100 macro-features alone: “Simplicity is highly overrated.”

Don’t Talk About Structural VAR

Guglietta isn’t keen on VAR (i.e., vector autoregression, a forecasting algorithm), calling it one of the model dependencies that produces only “shallow algorithmic subtleties.”

Do Quote the Greats

Guglietta loves Alan Turing, the cryptographer who famously broke the Nazi Engima code. He also loves Ray Solomonoff, the inventor of algorithmic probability. Quoting these mathematics greats could very well break the ice with any machine-learning or A.I. researcher/inteviewer. For example: “Learning is compressing complexity by accepting a given amount of uncertainty,” is from Solomonoff.   

Guglietta also likes the concept of Occam’s Razor (“Models should not be multiplied beyond necessity”) and the principle of multiple explanations, which says that if more than one model is consistent with observations, keep them all.

You should also be familiar with Kolmogorov complexity and Bayes Probability Theorem. Guglietta is also a fan of the concept of the ‘centaur’: A notion introduced by chess champion Garry Kasparov to describe the benefits of combining of human and machine intelligence (in essence: A computer can beat a human at chess, sure… but a human partnered with an A.I. platform can beat a solo computer without a problem).

Don’t Speak in Favor of Data for the Sake of Data 

Not all data is made the same. “Where we are not sure about the data we don’t use the data,” Guglietta said. “You always trade order. You don’t trade noise. If you are trading noise, stop.”

Don’t Presume You Can Do It All

Guglietta also had a few things to say about the notion propagated by Marcos Lopez de Prado that data scientists without finance domain knowledge can devise trading models simply based on mathematical patterns. 

“If you expect to put 50 data scientists in a room and to find a solution, then good luck,” Guglietta said. “You need traders, economists… you need [finance] expertise. Data scientists on their own will not find a solution.” In other words, more knowledge is always better. Keep that in mind when interviewing specifically for finance IT positions.

This article originally appeared in eFinancialCareers.