Machine Learning Researcher

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.

At Alipes, 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

• Competitive benefits, including pension, health insurance, and 30 days of vacation.

• A modern, harbour front office in Copenhagen (Nordhavn) with a flexible hybrid work model.

• A flat hierarchy with high levels of trust, autonomy, and freedom to make decisions.

• A valued role with unique responsibilities and plenty of room for creativity.

• Fast feedback and recognition of your contributions in a high-growth company.

• Strong team spirit - successes are celebrated with company events and trips.

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 [email protected]. Please note that applications should not be sent to the email address but rather via the job link.

Due to summer holidays, our response times may be a little longer than usual. We appreciate your understanding and look forward to reviewing your application.