Senior Data Scientist

Wymagania:
- Solid, hands-on experience in Data Science, Machine Learning, or quantitative research (a plus if it includes finance, time-series modeling, stochastic calculus or decision-making under uncertainty).
- Working knowledge of current ML methods, and the ability to read research papers and turn them into working implementations.
- A solid grasp of probability theory and statistics - distributions, estimation, Bayesian inference, hypothesis testing - and of the mathematical/statistical foundations of ML algorithms (optimization, regularization, attention mechanisms, encoder-decoder architectures).
- Strong Python skills, with practical experience in core numerical, statistical, and visualization libraries (NumPy, pandas, SciPy, scikit-learn, Matplotlib), optionally ML frameworks (Tensorflow, PyTorch, JAX).
- Ability to design, implement, and validate probabilistic or forecasting models using methods such as quantile regression, GARCH-type models, and Monte Carlo simulation.
- Ability to write clean, testable, well-documented code and to work comfortably with Git.
- Ability to work independently and take initiative in deciding the next steps of a project.
- Genuine interest in understanding financial markets and investment instruments, and in applying Data Science or ML knowledge to real-world problems.
- Willingness to adapt existing ML methods, or develop new ones, to meet the specific challenges of the finance industry.
- We require proficiency in both Polish and English, spoken and written, at a minimum B2 level.

Mile widziane:
- Experience with optimization methods (e.g., genetic/evolutionary optimization).
- Hands-on experience with probabilistic programming (PyMC, Stan, NumPyro).
- Basic knowledge of how financial markets and financial instruments work.
- Experience running computations on compute clusters or remote servers.
- Research publications or independent projects in quantitative finance.
- Experience working with AI-assisted research/coding tools (agentic coding assistants).

O firmie:
- We are one of the first companies in Europe and in Poland focused on developing quantitative investment methods based on machine learning. Our system is now being used by large US-based funds. For more than 8 years we have been building novel investment tools based exclusively on machine learning, statistical modeling, and advanced optimization methods. We work with major investment funds and brokers from the United States and Europe. Our vision is the practical application of the scientific method and the latest technology to profitable, safe investing. We are also actively involved in European and national research projects and collaborations with international research institutions.

Zakres obowiązków:
- Design and implement mathematical, statistical and machine learning models that forecast the probability for future financial events.
- Conduct data analyses that support investment decisions.
- Build and implement statistical and Bayesian models - e.g., GARCH-type volatility models, quantile regression, conditional-distribution models, and Monte Carlo methods.
- Carry out in-depth analysis of ML algorithms with respect to their effectiveness and limitations.
- Backtest and validate models; assess forecast quality through calibration checks, statistical tests, and error metrics.
- Optimize existing models for speed, accuracy, and scalability.
- Write clean, well-documented Python code.
- Prepare concise research documentation - assumptions, formula derivations, conclusions - that is easy to follow for a STEM-educated reader, and present findings internally.
- Collaborate with the investment team to identify and solve analytical problems, and set your own goals and priorities within projects.

Oferujemy:
- Work in an experienced team, with direct exposure to leading experts in machine learning and investing.
- Opportunities to grow your expertise and present research at scientific and industry conferences, mentoring and technical guidance from experienced leaders in respective fields.
- Creative, research-driven work with a high degree of autonomy over your research process and direction.
- Attractive compensation with a performance-linked bonus system.
- A friendly work environment, reflected in very low staff turnover.
- Hybrid work (1 day in the office, 4 remote) and flexible working hours.