Location: Copenhagen, Denmark (On-site, 1 day per week flexibility to work from home)
Salary / Hourly Rate: $150,000 - $190,000 per year
Benefits: 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
Role Information
Role Overview: N/A
Responsibilities: 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., 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.
Qualifications: 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., 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.
Minimum Requirements: PhD would be preferred, not a strict requirement,No strict rule on academic institutions - but they need to be good universities (at least top 100) (https://www.timeshighereducation.com/world-university-rankings/2026/subject-ranking/computer-science),Important that candidates show they are not purely academic and are comfortable in a practical, coding-focused environment.,Atleast 3 years of work experience
Screening Questions: N/A
Company Information
About Company: N/A
Culture: N/A
Additional Information
Interview Process: We first send candidates a Python test via Coderpad for them to complete., If they pass, we invite them for an online 30-min introductory meeting with the hiring manager, where they will be asked some comp fundamental questions and review some code., If they pass that, they will be invited for an in-person or remote interview with the hiring manager and Head of Equities Quant., The last part of the process is a Workday, where they will be invited to the office for a full day, and work on an assignment, and present it at the end of the day.
Day to day: As a Quantitative Researcher at Alipes, you will spearhead a data-centric workflow to capture alpha in equity markets through statistical arbitrage. Operating within a high-autonomy environment, you will bridge the gap between advanced machine learning and market microstructure to build scalable, risk-managed portfolios that thrive on high-frequency dynamics and sophisticated execution logic.
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 modeling and machine learning. The stat-arb sub-team is a tight group of 3 researchers.
Growth: Growth opportunities are dependent on how the individual develops and performs. We don't have a structured career ladder or similar, but in general our teams have autonomy and quants can dive into areas they find interesting. If we continue growing there may be opportunities for leading a team in the future.
Ideal Candidate Profile: 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.
