Location
Remote, Tashkent
Company
Works on technology projects for clients including PayPal, Wargaming, Xerox, Philips, adidas, and Toyota.
What you will do
•Develop and validate machine learning models for financial time series and cross-sectional data.
•Research alpha signals, feature engineering, and predictive modeling techniques.
•Design statistically rigorous experiments and backtesting frameworks.
•Work with large-scale structured and unstructured financial datasets and improve data pipelines, labeling strategies, and evaluation methods.
•Collaborate with engineering teams to deploy models into production pipelines.
•Analyze model performance, stability, and robustness under changing market conditions.
Requirements
•3+ years of relevant experience.
•Strong Python skills and experience with ML ecosystems including AWS SageMaker and MLflow.
•Hands-on experience with tabular and time-series data using machine learning.
•Knowledge of supervised learning, feature engineering, model evaluation, overfitting, regularization, and cross-validation.
•Knowledge of statistical methods and probability theory, with experience in 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 skills, with the ability to clearly communicate findings and trade-offs.
•Ownership of tasks from research through implementation, plus 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 or time-series data, including Transformers and RNNs.
•Experience with probabilistic modeling or Bayesian methods.
•Hands-on experience with production ML systems, including MLOps, monitoring, and retraining.
•Ability to define research direction, identify high-impact opportunities, make decisions under uncertainty, and translate business problems into ML solutions.
Culture & Benefits
•Remote work options.
•Competitive compensation based on qualifications and skills.
•Career development system with clear skill qualifications.
•Medical expense compensation, online English courses, and gym membership compensation.
•Internal conferences, workshops, meetups, corporate parties, and sports competitions.
•Five paid sick days per year without requiring a sick-leave certificate.
