We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.

office remote European UnionGeorgiaKazakhstanPolandUzbekistan

Requirements

- 3+ years of relevant experience
- Strong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)
- Hands-on experience working with tabular/time series data with usage of ML
- Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross-validation
- Knowledge of statistical methods and probability theory
- Experience with experiment design and offline evaluation
- Ability to work with large datasets and build efficient data processing pipelines
- Familiarity with SQL and data querying
- Strong analytical and problem-solving mindset
- Ability to clearly communicate findings and trade-offs
- Ownership of tasks from research to implementation
- Curiosity and willingness to explore new approaches
- English proficiency at B1 level or higher

Nice to have

- Experience in financial machine learning, quantitative finance, or trading systems
- knowledge of signal generation, alpha research, portfolio construction or risk modeling
- Experience with:



Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods
- Hands-on experience with production ML systems (MLOps, monitoring, retraining)
- Ability to define research direction and identify high-impact opportunities
- Decision-making under uncertainty
- Ability to translate business problems into ML solutions

Responsibilities

- Develop and validate machine learning models for financial time series and cross-sectional data
- Conduct research on alpha signals, feature engineering, and predictive modelling techniques
- Design experiments and backtesting frameworks with proper statistical rigor
- Work with large-scale structured and unstructured financial datasets
- Collaborate with engineering teams to deploy models into production pipelines
- Analyze model performance, stability, and robustness under changing market conditions
- Improve data pipelines, labeling strategies, and evaluation methodologies

We offer

- Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
- Competitive compensation that depends on your qualification and skills
- Career development system with clear skill qualifications
- Versátil working hours aligned to your schedule
- Options to work remotely

Any questions?

Veronika [email protected]

📌 ML Middle/Senior Engineer (Trading) (La Unión)
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📍 La Unión