— Python 3.6+;
— Understanding and experience with REST;
— Prior experience of Pandas, scikit-learn, Keras.
— Selective implementation of algorithms developed in Julia language and R;
— Math background;
— Docker, Kubernetes;
— Experience in DataScience, Machine Learning, Quantitative Trading.
— Competitive salary based on the results of the interview;
— Challenging tasks and professional growth;
— Paid vacations, days off and sick leaves;
— Healthcare (50% compensation);
— Comfortable workplace, office kitchen, and rest area;
— Company English classes (50% compensation);
— Professional workshops and seminars attendance (50% compensation);
— Any sports activities (50% compensation);
— Regular outdoor activities and team buildings events;
— Inspiring Friday breakfasts with colleagues;
— Possibility to work from home 10 days per month;
— IT Club bonus program;
— Relocation assistance to nonresidential job seekers.
— Own and lead the development of a forecasting library, which provides automating forecasting tools exposed via API;
— Work with our academic research partners to develop a set of abstract classes that will be used to implement all of the forecasting methods, and a set of highly robust tests to ensure the performance of all methods;
— Implement a set of 50+ univariate and multivariate forecasting methods identified by our academic research partners as highly promising;
— Expose forecasting methods via API framework (Fast API or similar) with Swagger for automated documentation of API endpoints. API endpoints must be secured with OAuth or similar authentication;
— The library must be highly performant and production-grade, including for example autoscaling workers, parallelization via packages such as Dask and use of Numba/Cython for highly performant algorithms.
Our сlient has multiple finance-related projects, one of them, for example, a forecasting library, which provides automating forecasting tools exposed via API.
Our client is focused on radical innovation for the world of investment management. They build and back disruptive products and businesses for institutional investing out of our office in Manhattan, New York. Areas of interest span all assets classes, including digital assets and new ways to invest in real assets, and all kinds of technology — they have projects leveraging machine learning, big data, and blockchain.