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.