Quantitative Researcher - Equity Statistical Arbitrage

Imagine working with cutting-edge machine learning models, crunching high frequency data sets and sparring with exceptional and fun colleagues, all while taking part in scaling our business even further.

Does this sound highly appealing to you? Then you might be our new Senior Quantitative Researcher in the Equity Statistical Arbitrage wing of Alipes Capital!

At Alipes Capital, we have been a market leader in equity and electricity algorithmic trading since starting in 2008.

This position is focused on

• Elevating our predictive modeling standards to the highest levels in the area of statistical arbitrage of traded funds.

• Creating a world class modeling workflow, enabling the team to turn massive data sets of financial information into mathematical models that accurately predict movements in the markets.

• In particular, the fixed-income section of our traded universe, in which we believe there is a significant untapped signal.

In doing so, you will get a chance to tackle a variety of software infrastructure challenges, such as:

• Development and optimization of calibration and benchmarking frameworks.

• Building out and implementing best practices for data preparation and dataset generation pipelines.

• Prototyping and releasing novel predictive architectures, especially with respect to time-series models.

You will get instant validation of your work and experience short feedback cycles, where going from inception to deployment can be a matter of hours. There will be no red tape to cut and no sales people to consult. It is pure play.

About the team

We maintain an informal atmosphere where technical discussions are valued, and you are encouraged to take ownership of, and pride in, your work. You will impact the direction of the team, prioritize your work and choose the tools that get the job done.

The Quant team consists of eighteen passionate quantitative researchers covering 6 nationalities, with thirteen PhD’s and five MSc’s with expertise in statistical analysis, mathematical modeling and machine learning. The stat-arb sub-team is a tight group of 3 researchers.

We enjoy collaboration and are always willing to lend a helping hand. We work alongside a team of software engineers and a team of traders that implement and conceive the mathematical models together with us. All team members across the organization write code and drive change and are equally willing to engage on a nitty-gritty technical level as well as discuss the larger strategic level.

About you

• Fluency within mathematics and statistics.

• Experience working with financial data, in particular valuation and hedging of fixed-income instruments.

• Experience working with the full Machine Learning stack from data generation through model calibration and real-life validation and monitoring.

• Programming experience with Python, in particular data-wrangling and numerical programming.

• Proficiency with computer science fundamentals.

• A scientific and inquisitive mind.

Nice to have

• Experience working with ‘out-of-core’ datasets.

• Experience working with tools like PyTorch, Tensorflow, XGBoost and/or Catboost.

• Programming experience with languages like C# and C/C++.

• PhD or MSc degree in engineering, physics, computer science, mathematics or economics.

What we can offer you

• Competitive benefits, including pension, health insurance, and 30 days of vacation.

• A modern office in Copenhagen (Nordhavn) with a flexible hybrid work model.

• A flat hierarchy with high levels of trust, autonomy, and freedom to make decisions.

• A valued role with unique responsibilities and plenty of room for creativity.

• Fast feedback and recognition of your contributions in a high-growth company.

• Strong team spirit - successes are celebrated with company events and trips.

Practicalities

To apply, please send us your CV and a cover letter telling us why you think this role is a great fit for you. If your profile looks like a good match, we will get in touch to coordinate an online technical test as a first step in the interview process.