Machine Learning Engineer

About Us

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 8 years, we have diligently fostered the largest Data Science Community in Eastern Europe.

About The Project

The project deals with financial forecasts and harnesses technology, data analytics, and deep market insights to deliver attractive, risk-adjusted returns for investors, regardless of market cycles or phases.

About The Role

We are looking for a Middle Machine Learning Engineer to join the team.

Requirements

– 3+ years of experience as a Machine Learning Engineer.

– Experience working with time series, LSTM, Arima, Gradient Boosting.

– Previous experience working in fintech is a must.

– BS/MS in AI, Computer Science, Mathematics, or related fields.

– Libraries and Frameworks: Experience with popular machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, Pandas, NumPy, XGBoost, and LightGBM.

– Experience with data visualization tools like Matplotlib, Seaborn, and Plotly.

– Experience of working with Cloud Services: AWS/GCP/Azure.

– Experience with basic machine learning algorithms (linear regression, decision tree, SVM, neural networks, classification, clustering, regression, etc.)

– Great experience working with Docker, Kubernetes, and CI/CD to automate model deployment processes.

– Knowledge of REST API for interacting with other services.

– Experience working with Agile/Scrum processes, knowledge of project management tools (JIRA, Trello).

– Knowledge of parallel computing on CPU/GPU and code optimization for large amounts of data.

Would Be a Plus

– Alpha Research & Strategy Development: Ability to translate data-driven insights into actionable investment signals.

– Backtesting & Validation: Experience with realistic back-testing frameworks that account for transaction costs, slippage, and market impact to ensure robust strategy performance.

– Simulation & Stress Testing: Familiarity with techniques to validate strategy performance across various market conditions, including scenario analysis, robustness checks, and more.

– Regime Adaptability: Knowledge of methods to adjust models dynamically based on changing market conditions, such as Bayesian inference, hidden Markov models, reinforcement learning, and other adaptive techniques.

– Equity & Market Expertise: Strong understanding of trading principles, portfolio construction, and execution dynamics in equity investing (not HFT).

– Financial Instruments & Market Exposure: Experience with stocks, indices, ETFs, and commodities, with a focus on medium-term investment horizons.

– Robust Model Development: Expertise in mitigating overfitting risk, applying regularization techniques, and implementing rigorous back-testing methodologies such as walk-forward testing.

We Offer

– Good compensation.

– 100% remote job.

– Strong team.

– The best Data Science community in the Eastern Europe.

– Challenges every day.