Machine Learning Researcher Time-Series & Financial Modeling

Alipes ApS; København; Hybrid; DKK 900,000 - 1,300,000; Full time

Benefits offered by this job

Pension

Health insurance

30 days vacation

Harbour-front office

Job summary

Alipes ApS in Copenhagen is seeking a Machine Learning Researcher to lead the development of a world-class modeling workflow, turning massive financial data into predictive models.

You will calibrate, benchmark and deploy time-series architectures, collaborate with a tight-knit team of researchers and traders, and enjoy a fast feedback, low-friction environment in a modern harbour-front office with hybrid work. The role emphasizes ownership and delivering impact with minimal red tape.

Qualifications

• Experience with the full ML lifecycle from data generation through model calibration and real-life validation and monitoring.
• Proficiency in Python for data wrangling and numerical programming.
• Strong mathematics and statistics foundation.
• Scientific and inquisitive mindset.

Responsibilities

• Lead the development of a world-class modeling workflow.
• Develop calibration and benchmarking frameworks.
• Build data generation pipelines and preprocessing steps.
• Prototype and release novel predictive architectures, especially time-series models.
• Collaborate with researchers and traders to align modeling with business goals.

Skills

Mathematics & statistics

Machine Learning stack

Python programming

Computer science fundamentals

Analytical mindset

Education

PhD or MSc in a relevant field

Tools

PyTorch

TensorFlow

XGBoost

Catboost

Job description

Alipes ApS in Copenhagen is seeking a Machine Learning Researcher to lead the development of a world-class modeling workflow, turning massive financial data into predictive models.

You will calibrate, benchmark and deploy time-series architectures, collaborate with a tight-knit team of researchers and traders, and enjoy a fast feedback, low-friction environment in a modern harbour-front office with hybrid work. The role emphasizes ownership and delivering impact with minimal red tape.