Position: Machine learning engineer/quantative analyst
Category: Data Science
English level: upper
Years of experience: 5

Startup from scratch - 24 microservices, Kuberbetes cluster, 2 databases, curator at time series data science course, electric unicycle big fan.
Timeseries Jedi. Advanced techniques in Deep learning, such as as the latest state of the art - DenseNets for noisy time series data forecasting, Ordinary Differential Equations as time series regressor, Wavelet transform to extract signal from noise, advanced financial time series (Marcos Lopes de Prado is my table book), have built startup from scratch for financial markets(24 micro services on Kubernetes cluster), timeseries course curator at Projector. I have my own features generation service, not dependable on data providers and data purity, I work with extremely noisy data with a very low degree of signal to noise ratio.
Timeseries or digital signal processing with a deep research part, no Dia city residents