IT data scientists are responsible for mining complex data and providing systems-related advice for their organization. They design new ways to incorporate vast information with a focus on information technology topics. They work with teams of other IT professionals to manage statistical data and create different models based on the needs of their company. They possess advanced analytical skills, in addition to their exceptional oral and written communication abilities. They process research information for easier consumption and transform it into actionable plans. They also provide value to their businesses through their findings and thoughtful insights.
IT data scientists follow specific, strict company and industry guidelines in their work. They observe data privacy rights to ensure client satisfaction and avoid legal issues. They create networks of professionals to consult, including internal partners and external colleagues. Most of the time data scientists work in teams using collaborative filtering, k-nearest neighbors, market basket analysis and matrix factorization methods. They deal with cutting edge technologies on a regular basis, and often have the best tools available at their disposal. One of their main work tools is usually an industrial computer with high processing power and proprietary software applications for research tasks.
IT data scientists usually must possess previous work experience in a similar position. They should have advanced knowledge of different data mining techniques such as clustering, regression analysis, decision trees and support vector machines. An advanced degree (such as a PhD) in computer science is usually required for this kind of position, in addition to previous years of work experience in a related field.
Data Scientist, IT Tasks
Design and build new data set processes for modeling, data mining, and production purposes.
Determine new ways to improve data and search quality, and predictive capabilities.
Perform and interpret data studies and product experiments concerning new data sources or new uses for existing data sources.
Develop prototypes, proof of concepts, algorithms, predictive models, and custom analysis.