Tether is a data driven startup that optimizes the charging patterns of Electric Vehicles parked throughout a city, aggregating them into the world's largest virtual battery that reinforces the stability of the power grid. We are looking for candidates with a background in Data Science or Machine / Deep Learning who want to develop predictive models for time-series data such as electricity market prices, EV driving behavior, or power system frequency.
One of the major challenges for Tether is the forecasting of not only the grid frequency and electricity prices, but also the charging patterns of each individual EV user. This position will consist on further developing time-series forecasting and consensus Machine-Learning models to accurately predict these parameters and ensure bid reliability. On top of that, we are looking for experts that can analyze, visualize, and create explanatory reports from the millions of data points we collect.
We want candidates that are impact driven, looking for a dynamic and collaborative work environment, and are interested in helping to develop the optimization algorithm at the heart of the company.
Keywords: #Python, #Machine-Learning, #Matplotlib, #Seaborn, #Plotly, #PowerBI, #Tableau, #Sci-kit-learn, #Tensorflow, #Keras
Organize, filter, and clean the relevant data on market prices, EV driver parking schedules, and grid frequency into usable datasets.
Build regression, Machine Learning, or Deep Learning models for complex, dynamic datasets, and derive stochastic scenarios from these.
Discover and visualize trends and correlations in datasets, and create explanatory reports that show the insights uncovered in the data.