QualificationsStatistical analysisPerlInformation securitySQLApache HiveMaster’s degree
Apply expertise in operations research, data science, and data mining to develop analytics solutions.
Work in partnership with internal stakeholders on projects to identify and articulate opportunities, see beyond the data to identify solutions that will heighten the quality of decision-making.
Collaborate with cross-functional data and product teams across business applications to access and manipulate data, explain data gathering requirements, display results, and build efficient and scalable analytics solutions.
Define, compute, track, and continuously validate business metrics with descriptive and predictive analytics.
Drive process changes based on robust analysis of operational data and user behavior to improve overall business performance.
Master’s degree in Operations Research, Industrial Engineering, Industrial and Systems Engineering, Computer Science, Human Computer Interaction, Computer Applications, Software Engineering, Computer Information Systems, Systems Engineering, Information Technology, Information Security, Information Networking, Computer Science & Engineering, Electrical and Computer Engineering, Mathematics, Engineering and Information Technology, Electrical Engineering, Computer Engineering, Electronic/ Electronics Engineering, Engineering, Applied Sciences, Mathematics, Physics, Statistics, or related field. 24 months of work experience is required in job offered or as a Data Scientist, Operations Research Analyst, Software Developer, Software Engineer (all levels), Software Development Engineer, Test Engineer, Quality Engineer, Systems Engineer, Computer Programmer, Data Scientist, Programmer/Analyst, Computer Systems Analyst, Web Developer, Web Application Developer, Project Engineer, Engineer, Member of Technical Staff, Technology Lead, Consultant, Assistant Consultant, Research Assistant, Intern, or related occupation. Experience must include 24 months involving the following:
Machine learning techniques
Working with large data sets and network-based data (TCP or HTTP)
Relational database (SQL or PL*SQL)
Developing in scripting language: PHP, Python, or Perl
Statistical analysis using R, MATLAB, SPSS, SAS, or Stata
Large scale data processing infrastructures using distributed systems (Hadoop, Hive, MapReduce, or MPI)
Quantitative analysis techniques: clustering, regression, pattern recognition, or descriptive and inferential statistics
Communicating and presenting results of data analyses
Mathematical modeling and algorithms development
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