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 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.

Qualifications:

Bachelor's Degree in mathematics, statistics or equivalent degree is required.
At least 4 years of work experience in data analytics and analysis.
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.
Structured Query Language and JavaScript.
In-depth knowledge of statistical methods and tests.
Extensive experience with statistical packages, such as MS Excel, SAS, and SPSS. Exceptional analytical skills.