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Expertise in the development of forecasting models (machine learning and stochastic predictive models) In-depth knowledge and skills to analyze large datasets using both statistical and advanced data mining/machine learning algorithms Analysis of large datasets from Plugged-in Midlands Project (PiM) and eBridge project related to Electric Vehicles charging events and EV trips (R studio, Matlab, SPSS, WEKA tool and RapidMiner software packages used) Cluster Analysis using k-means algorithm to extract typical profiles (Matlab, R) Statistical Analysis using Regression models to investigate the long-term trend of a time series (Matlab, SPSS) Development of a quantitative risk analysis (decision making) model (based on Fuzzy-Logic) to identify the potential risk of EV charging demand on a distribution network (Matlab) Spatial Data Analysis (using Google Earth/Google Maps, Open Street Maps) Data Visualization experience (UML diagrams, Matlab, Microsoft Office) Professional experience using tools and techniques to utilize Business Intelligence in order to drive competitive advantages and create new strategic business opportunities Organizational and Analytical skills, Strong commitment to learn and a strong drive to achieve results, Intellectual skills and creative thinking, Ability to work independently and within a team environment and deal in a challenging environment under pressure, Strong independent decision-making, organizational, planning and problem-solving skills
Software developer, software engineer and quantitative developer interested primarily in C++ positions but will consider python. Masters degree in Financial Mathematics and experienced in C++ development.
A Data Scientist with a background in mathematics and statistics and experience in NLP, Image Processing, engineering data in datasets, and machine learning methods such as Bayesian Machine Learning, Neural Networks, and other algorithms such as Random Kitchen Sink, Regression/Random Forests, and Generalised Linear Models.