Demo

Machine Learning Researcher

POSTED ON 4/13/2025 AVAILABLE BEFORE 5/10/2025
Alipes ApS Copenhagen, Capital Region of Denmark Other

Imagine working with cutting-edge machine learning models, crunching high frequency data sets and sparring with exceptional and fun colleagues, all while taking part in scaling our business even further.

Does this sound highly appealing to you? Then you might be our new Machine Learning Researcher at Alipes Capital.

At Alipes Capital, we have been the market leader in automating the financial markets since we started back in 2008, shaping a fully automatic natural language processing system for reading and interpreting financial news.

To accomplish our mission of being the best at what we do, we understand the importance of elevating our predictive modeling standards to the highest levels. Your work will involve leading the efforts in creating a world class modeling workflow, enabling the team to seamlessly turn massive data sets of financial information into mathematical models that accurately predict movements in the financial markets.

In doing so, you will get a chance to tackle a variety of software infrastructure challenges, such as:

  • Development and optimization 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.

About the team

All of this will happen in an informal atmosphere where technical discussions are valued, and you are encouraged to take ownership of, and pride in, your work. You will impact the direction of the team, prioritize your work and choose the tools that get the job done. We are a tight-knit modeling team with fourteen passionate quantitative researchers covering 6 nationalities – eleven PhD’s and three MSc’s with expertise in statistical analysis, mathematical modeling and machine learning. We enjoy collaboration and are always willing to lend a helping hand. We are working alongside two other teams of software developers and traders that implement and conceive the mathematical models together with us. What makes this constellation work great is that all of the individual team members write code and understand that a seemingly trivial change can actually require a lot of work, and vice versa.

About you

  • Fluency within mathematics and statistics

  • 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

What we can offer you

  • Flat hierarchy and high levels of trust and autonomy

  • Exciting problems to solve

  • Collaborative team made up of really smart and curious people

  • Nice office in Nordhavn with a hybrid work model

  • Pension, Health Insurance and 30 days of vacation

Practicalities

To apply, please send us your CV and, in lieu of providing a cover letter, concisely answer the questions on the application. If your profile looks like a good match, we will get in touch to coordinate an online technical test as a first step in the interview process.

Should you have questions about the role that you would like clarified before applying, you're welcome to send an email to quant_jobs@alipescapital.com. Please note that applications should not be sent to the email address but rather via the job link. 

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