LR078 Data Scientist II

Data Scientist II, Cloud Computing Technologies Agile Development Labor Rate create custom data models and algorithms, 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. Being a Data Scientist II 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. Work with stakeholders to identify opportunities for leveraging company data to drive business solutions. Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies. Develop custom data models and algorithms to apply to data sets. Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes. Develop company A/B testing framework and test model quality. Assess the effectiveness and accuracy of new data sources and data gathering techniques. In addition to a Bachelor science degree, Data Scientist II may require an advanced degree (masters or Ph.D.) in computer science, Data Processing, mathematics or any other related field with 6+ years of working experience in Data science or related field. Typically reports to a manager. The Data Scientist II is occasionally directed in several aspects of the work. Data Scientist II should have Knowledge of Machine Learning techniques, including decision tree learning, clustering, artificial neural networks, etc., and their pros and cons. Knowledge and application experience in advanced statistical techniques and concepts, including, regression, distribution properties, statistical testing, etc. Good communication skills to promote cross-team collaboration Impulse to learn and master new technologies. Multilingual coding knowledge/experience: Java, JavaScript, C, C++, etc. Experience/knowledge in statistics and data mining techniques, including, random forest, GLM/regression, social network analysis, text mining, etc. May have experience with major web services, including S3, Spark, Redshift, etc.

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