Quantitative Researcher - Equity Statistical Arbitrage

Job Overview

As a Quantitative Researcher within the Equity Statistical Arbitrage wing, you will spearhead a data-centric workflow to capture alpha by elevating predictive modeling standards to the highest levels. This role focuses on creating a world-class modeling workflow that transforms massive financial datasets into mathematical models, with a specific emphasis on uncovering untapped signals within the fixed-income section of the traded universe. You will tackle complex software infrastructure challenges, including the optimization of calibration frameworks, the implementation of robust data preparation pipelines, and the prototyping of novel time-series architectures. Operating in a high-autonomy environment with short feedback cycles, you will bridge the gap between advanced machine learning and market microstructure to build scalable, risk-managed portfolios. The ideal candidate possesses at least three years of professional experience and demonstrates a scientific, inquisitive mind that balances theoretical depth with practical implementation. You should be highly proficient in mathematics, statistics, and the full machine learning stack, from data generation through to real-life validation. Strong programming skills in Python for data wrangling and numerical programming are essential, as this is a development-heavy role requiring evidence of real-world application rather than a purely academic focus. While a PhD from a top-tier university is preferred, we value candidates from physics, engineering, or computer science backgrounds who have experience in the valuation and hedging of fixed-income instruments. Success in this position requires the ability to engage on a nitty-gritty technical level while driving change across the broader modeling strategy.

Responsibilities:

About the Team

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 modelling 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.

The Role

We are seeking a Quantitative Researcher to join the Equity Statistical Arbitrage wing of Alipes Capital. This position is focused on:

• Elevating our predictive modelling standards to the highest levels in the area of statistical arbitrage of traded funds.
• Creating a world class modelling 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 optimisation 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.

The monthly compensation range is DKK 80-90K for candidates with 3-5 years workex and DKK 90-100K for candidates with 5-10 years workex.

Qualifications

What we're looking for:

• Candidates with around 3 years' experience or more (some flexibility depending on overall strength of profile) - we have rejected many candidates for being "too junior".
• 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.

Ideal Candidate

Ideal candidate:

• This is a development-heavy role, with a strong focus on hands-on programming and working with data (ideally financial data).
• Open to candidates straight out of a PhD, provided they have significant practical experience (e.g. applied projects, coding, data work) and ideally some software development exposure within finance.
• Candidates should not be purely theoretical - we need evidence of real-world application and implementation experience.
• No strict requirement for a Computer Science degree; candidates from Maths, Physics, Engineering, or similar quantitative backgrounds are suitable if they can demonstrate solid development skills and basic CS knowledge.
• Important that candidates show they are not purely academic and are comfortable in a practical, coding-focused environment.
• Financial experience is preferred, particularly working with financial data.
• Ideal candidates will have experience in valuation and hedging of fixed-income instruments, but this is not strictly required.
• There is flexibility on domain experience - candidates from other areas of finance (e.g. risk) can be considered.
• Ultimately, decisions will be based on the overall strength of the candidate (full package), balancing technical ability, practical experience, and domain knowledge.

Benefits 

Retirement/401k

Health Insurance

Vision Insurance

Dental Insurance

Company paid pension (5%) 30 days of holiday + bank holidays

Loads of social events, wellbeing initiatives and great food Bonus: 1 month of salary based on company trading profits, paid out at the end of the year

For candidates who're relocating, we would offer services from a mobility company called Relocation Scandinavia, as well as 1 additional month of salary (paid out on the first payslip).

The mobility company would assist with practicalities, processing work permit and finding an apartment

Relocation & Sponsorship

Relocation AssistanceVisa

Sponsorship