Position: Data Science Lead
Category: Data Science
English level: upper
Years of experience: 10

PhD in Computer Science (Artificial Intelligence Aids).
I have about 10 years experience in artificial intelligence aids. I implemented an intellectual decision support system in my PhD thesis. My hands-on experience and core expertise include predictive models development, implementation of supervised/unsupervised learning algorithms, customer insights analysis, data analytics.
Looking for Data Science Lead position 

Insurance. 
Risk prediction for motor insurance companies 
Key responsibilities (KR): EDA, supervised/semi-supervised outlier detection (boosting and multilayer neural networks algorithms), multivariate data visualization (t-SNE, umap), explainable AI models development.   
Technologies: Python, Tensorflow 

Healthcare. 
Outlier detection in clinical trials
KR: clinical data preprocessing, EDA, data clustering (k-means clustering, Isolation Forest, DBSCAN etc.), dimension reduction, outlier detection with unsupervised learning algorithms and neural networks (Variational Autoencoder). 
Technologies: TensorFlow, Keras, Python (pandas, numpy, sklearn), R (dplyr, ggplot2)

Sales.
Machine learning for Customer Insights
KR: sales data preprocessing, EDA, feature generation, prediction model development, model validation, feature importnabce and correlation analysis, demo presentation. 
Technologies: Python, pandas, XGBoost

University.
Teaching, academic and research activities.
KR: developing courses "Intellectual data analysis", "Decision making theory", "System modelling", "Software development technologies", "System analysis", "Information processing systems", "Support decision making systems"; mentoring students ( conferences, contests, hackathons, theses); scientific research activity (publication in journals, attending conferences); organizing of international scientific conferences and IT-hackathons
Technologies: Python (pandas, numpy, scikit-learn), R, MS Azure, UML, Matlab, FuzzyLogic Toolbox

Business
Personnel assessment. Decision making system for the HR department  in order to reduce staff turnover.
KR: system analysis, initial data processing, feature generation, intellectual models development, rule base development, model evaluation and validation.
Technologies: R(dplyr, ggplot2, fuzzyR), machine learning (svm, random forest)

Commercial bank
Intellectual decision support system development
KR: data collection from different sources, EDA, predictive analysis, quality measurement model development, financial results prediction model development, fuzzy logic model development, validation, banking processes formalization.
Technologies: Matlab, FuzzyLogic Toolbox, BPMN, UML