Senior Quant Researcher – Equity Stat Arb & Time-Series ML

Alipes ApS; København; Hybrid; DKK 900,000 - 1,200,000; Full time

Benefits offered by this job

Pension

Health insurance

30 days vacation

Hybrid work model

Flat hierarchy

Job summary

Alipes Capital in Copenhagen seeks a Senior Quantitative Researcher for equity statistical arbitrage. You will elevate modeling standards and build an industry-leading workflow that turns large financial data into robust market forecasts.

The role focuses on fixed-income signals, calibration frameworks, and time-series architectures, with a strong emphasis on Python and end-to-end model validation in a fast-growing, collaborative environment.

Qualifications

• Fluency in mathematics and statistics.
• Experience working with financial data, in particular valuation and hedging of fixed-income instruments.
• Experience with the full Machine Learning stack from data generation to calibration and validation and monitoring.
• Programming experience with Python.
• Proficiency with computer science fundamentals.
• A scientific and inquisitive mind.

Responsibilities

• Elevate predictive modeling standards in statistical arbitrage of traded funds.
• Create a world class modeling workflow turning massive financial data into mathematical models.
• Focus on fixed-income signals within the traded universe.
• Develop calibration and benchmarking frameworks.
• Build data preparation and dataset generation pipelines.
• Prototype and release novel time-series predictive architectures.

Skills

Mathematics

Statistics

Financial data

ML stack

Python

Computer science fundamentals

Scientific mindset

Education

PhD or MSc in engineering/physics/CS/math/economics

Tools

PyTorch

TensorFlow

XGBoost

CatBoost

C#

C/C++

Job description

Alipes Capital in Copenhagen seeks a Senior Quantitative Researcher for equity statistical arbitrage. You will elevate modeling standards and build an industry-leading workflow that turns large financial data into robust market forecasts.

The role focuses on fixed-income signals, calibration frameworks, and time-series architectures, with a strong emphasis on Python and end-to-end model validation in a fast-growing, collaborative environment.