ML разработчик

Itransition provides software development and IT consulting services for global clients across various industries, including finance, retail, and technology.

• 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.

• 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 for efficient technical and business communication;
• 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.

• Competitive compensation based on qualification and skills;
• Career development system with clear skill qualifications;
• Flexible working hours.