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ML Middle/Senior Engineer (Trading)

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MidZdalnieFull-time

#468843·Dodano 7 dni temu·1

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Tech Stack / Keywords

PythonAWS SageMakerMLflow SQLTransformersRNNsMLOps

Podsumowanie oferty

Develop and validate machine learning models for financial markets, including research into alpha signals and predictive methods. Work with financial time-series data and collaborate on production deployment, using Python and ML ecosystems such as AWS SageMaker and MLflow.

Wymagania

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 using ML.
Solid understanding of machine learning fundamentals: supervised learning, feature engineering, model evaluation, overfitting, regularization, and 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.

Obowiązki

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.

Benefity

Flexible working hours aligned to your schedule.
Career development system with clear skill qualifications.

Elastyczne godziny

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