Senior Quantitative Researcher, Equity Stat Arb

Alipes ApS; København; Hybrid; DKK 1,200,000 - 1,800,000; Full time

Benefits offered by this job

Competitive benefits

Hybrid work model

Flat hierarchy

Creativity-friendly environment

Team events and trips

Job summary

Alipes Capital is seeking a Senior Quantitative Researcher to lead statistical arbitrage modeling for equity instruments. You will handle predictive modeling, data pipelines, and time-series architectures in a collaborative, fast-paced environment.

The role emphasizes developing calibration and benchmarking frameworks, working with Python, and applying ML tools to financial data. A flexible hybrid office setup in Copenhagen supports this high-growth team.

Qualifications

• Fluency in mathematics and statistics.
• Experience with financial data, valuation and hedging of fixed-income instruments.
• Experience with full Machine Learning stack from data generation to model validation.
• Programming in Python, esp. data wrangling and numerical computing.
• Solid CS fundamentals.
• Curious, scientific mindset.

Responsibilities

• Elevate predictive modeling standards in statistical arbitrage of traded funds.
• Create modeling workflows to turn large financial datasets into predictive models.
• Develop and benchmark calibration/frameworks and data pipelines.
• Prototype and release time-series forecasting architectures with validation and monitoring.
• Collaborate with software engineers and traders to deploy models quickly.

Skills

Mathematics

Statistics

Financial data

ML stack

Python

CS fundamentals

Scientific mindset

Education

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

Tools

PyTorch

TensorFlow

XGBoost

Catboost

C#

C/C++

Job description

Alipes Capital is seeking a Senior Quantitative Researcher to lead statistical arbitrage modeling for equity instruments. You will handle predictive modeling, data pipelines, and time-series architectures in a collaborative, fast-paced environment.

The role emphasizes developing calibration and benchmarking frameworks, working with Python, and applying ML tools to financial data. A flexible hybrid office setup in Copenhagen supports this high-growth team.