LR079 Data Scientist III

Data Scientist III, Cloud Computing Technologies Agile Development Labor Rate identifies business trends and problems through complex big data analysis. Interprets results from multiple sources using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining independently. Data Scientist III designs, develops and implements the most valuable business solutions for the organization. Prepares big data, implements data models and develops database to support the business solutions. Formulate business problems into optimization ones. Work on large datasets, gather insights and develop machine learning models. Develop, train and evaluate ML models. Coordinate with different functional teams to implement models and monitor outcomes. Develop processes and tools to monitor and analyze model performance and data accuracy. In addition to a Bachelor of science degree, Data Scientist III may require an advanced degree (masters or Ph.D.) in computer science, Data Processing, mathematics or any other related field. The Data Scientist III work is generally independent and collaborative in nature. Contributes to moderately complex aspects of a project. To be a Data Scientist III typically requires 9+ years of related experience. Should have Programming experience with a scripting language such as Python, Perl, C/C++ or Ruby. Knowledgeable in machine learning algorithms and experienced in building production pipelines. Experience with visualization tools and techniques. Possess good analytical skills. Experience performing data mining, analysis, and training set construction. Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc. Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications. Experience with distributed data/computing tools: Map/Reduce, Hadoop. Typically reports to the Data science manager.

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