Pay rate range - $50/hr. to $53/hr. on W2 Expectation is fully onsite, but there is some flexibility for a hybrid schedule. Must Have Data Analysis and Modeling Experience Database Engineering experience Data Pipelines and Automation Experience Job Qualifications: - 3-6 years of experience working with large-scale complex datasets - Strong analytical mindset, ability to decompose business requirements into an analytical plan, and execute the plan to answer those business questions - Strong working knowledge of SQL and Python - Background (academic or professional) in statistics & programming - SAS experience is a plus - A graduate degree in math/statistics, computer science or a related is highly desirable. - Excellent communication skills, equally adept at working with engineers as well as business leaders Job Description: A successful candidate will have strong data mining and modeling skills and is comfortable facilitating ideation and working from concept through to execution. This role will also build tools and support structures needed to analyze data. As a BIE in this team, you will be responsible for developing the data architecture components that scale for the ever-evolving data needs. You will apply cloud-based AWS services to solve challenging problems around big data processing, data warehouse design, and enabling self-service. You will focus on automation and optimization for all areas of Databases/DW/ETL maintenance and deployment. You will work closely with software development teams on many non-standard and unique business problems and use creative problem-solving to deliver actionable output. Key Responsibilities: - Evaluation of the performance of program features and marketing content along measures of customer response, use, conversion, and retention - Statistical testing of A/B and multivariate experiments - Design, build and maintain metrics and reports on program health - Respond to ad hoc requests from business leaders to investigate critical aspects of customer behavior, e.g. how many customers use a given feature or fit a given profile, deep dive into unusual patterns, and exploratory data analysis - Employ data mining, model building, segmentation, and other analytical techniques to capture important trends in the customer base - Participate in strategic and tactical planning discussions