Quality Data Analyst

Responsibilities:

Utilize various data streams to establish failure rate trending related to YETI's warranty issues and pre-shipment inspection results
Use data mining to extract information from data sets, identify correlations and patterns, ultimately driving to root cause(s) and actionable information
Establish and maintain a database of inspection results, analyze the data, and prepare regular summary reports for quality leadership team related to YETI's quality KPI's
Establish and maintain data visualization and problem solving tools (A3, 5 Whys, etc) to establish and monitor continuous quality improvement activities
Provide input to Quality, Manufacturing and Engineering teams for new product development and product improvement programs
Review and improve data and analytics processes, methods and tools to increase efficiency and accuracy
Organize and transform information into comprehensible formats across multiple functions in the business
Identify and recommend new data gathering, management and analytic business processes
Communicate with stakeholders to understand data needs, content and business requirements
Evaluate and recommend new software for driving systems associated with data analytics and reporting
Liaise with YETI Customer Experience Group and 3rd party warehouse(s) to drive accurate and timely recording and collection of data

Qualifications:

Bachelor's Degree in mathematics, statistics or equivalent degree is required.
Expert knowledge of various software tools, including Microsoft Office Tools, Tableau, SAP, Salesforce, Smartsheet, etc.
Prior experience in creating, managing and maintaining databases and/or data sets.
Hands on experience utilizing standard problem solving tools and representations, A3 format preferred.
Strong ability to translate data and communicate results, findings, trends and issues to the cross-functional teams.
Past experience managing data related to key performance indicators related to quality.
Skilled analyst with excellent math and logic skills, as well as recognizing patters and trends within large data sets.

Key Skills:

Proficiency in programming languages, including Structured Query Language.
SQL, JavaScript.
In-depth knowledge of statistical methods and tests.
Experience with statistical packages, such as MS Excel, SAS, and SPSS. Exceptional analytical skills.