--Analyze on system or data processes, data structures, analytical methods, and applying key techniques
of data sciences (machine learning,Deep Learning, data mining, analysis) to build high quality predictive systems
--Exposure to SQL and databases
--Manage the codebase using Github
--Work with the business to apply scientific methods and to evolve data science solutions
- Development of graphic complexes in the field of data visualization;
- Creation of a vertical Data Visualization tool with respect to the various sources that will feed them;

• Identified causes of network problems through research and analysis, and developed and deployed
effective solutions quickly.
• Combine IT analysis with review of business and marketing methods to create comprehensive
strategy for improving productivity and profit margin.
• Collect and analyze data using calculations, graphical projection models, and other methods as
necessary to understand current operations and determine areas for improvement.

• All the typical phases of a data science project (business understanding, hypothesis, data transformation, metric analysis, predictive model);
• Machine Learning, Deep Learning, CNN, RNN with Python.
• Python ML / DL libraries and frameworks (eg NLP, Caffe, TensorFlow, Keras, OpenCV,Time series,
PyCaret, SweetViz, Sklearn, Numpy, Pandas, Matplotlib, Seaborn etc.), Flask;
• Implementation and use of the main models of supervised and unsupervised learning: regression, classification, forecasting, clustering, dimensionality reduction, recommendation, natural language processing, business intelligence;
• Main data manipulation techniques: cleansing, integration, normalization, anomaly detection, null imputation, features engineering;
• Programming C#.net (Visual Studio) MVC.
• RDBMS (SQL-SSMS, PostGre SQL), NoSQL(MongoDB)
• Working knowledge of Business Intelligence tools (MS BI, QlikSense)