Quality Data Analyst

About Turtlebox Audio The first Turtlebox was born out of necessity. After breaking every "loud" product on the market through routine outdoor use, the idea of the Turtlebox was born in our garage, not as a business but just for personal use. Nothing else could get the job done. Most portable speakers aren't actually meant for the outdoors and can't withstand the elements, the ones that can aren't loud enough to be heard. When you're at the beach or on an ATV, you need sound to carry in order to hear it over the wind, waves, and boat engines, you need a Turtlebox. We soon realized that there was a huge need for the Turtlebox among likeminded adventurers and our journey began to set out to make the world's loudest most rugged outdoor portable speaker. Click here for a peek inside our workplace! Role Overview: The Quality Data Analyst turns quality data into decisions. This role analyzes the company's quality data, including warranty return rates, customer feedback, product reviews, and factory operational KPIs, to identify trends, uncover root causes, and drive improvements in products, operational processes, and business strategy. Working closely with Quality, Engineering, Operations, Customer Service, and Supply Chain teams, the analyst builds dashboards that give clear visibility into how current systems and products are performing, designs experiments to determine the best configurations or operational changes, and applies predictive modeling to improve customer experience, operational excellence, and other business outcomes. The analyst tracks quality performance over time and delivers clear, data-backed recommendations to stakeholders at every level of the organization. Key Responsibilities: Data analysis and preparation Collect, combine, and analyze quality data from multiple sources, such as warranty claims, returns and RMA records, customer service tickets, product reviews, factory inspection results, and supplier data. Review data structures and identify errors, inconsistencies, and gaps before analysis begins. Clean and prepare data by correcting errors, removing duplicates, and handling missing values, documenting each step so results are repeatable. Identify trends, patterns, and outliers in failure modes, defect rates, and return reasons, and dig into the root causes behind them. Validation and testing Run validation checks to confirm data meets business rules and quality standards. Build and maintain automated data quality checks so issues are caught early rather than discovered in reports. Design and run experiments (A/B tests, process trials, design-of-experiments studies) to evaluate proposed product or operational changes and measure their impact. Dashboard development and reporting Develop and maintain dashboards that give leadership and cross-functional teams real-time visibility into quality performance, from the factory floor to the customer's hands. Automate recurring reports (weekly, monthly, and quarterly quality reviews) to reduce manual work and improve accuracy. Translate complex findings into clear, concise summaries and visuals for both technical and non-technical audiences. KPI monitoring and predictive analytics Track key quality metrics such as warranty return rates, customer returns, defect rates, first-pass yield, and other operational KPIs, and flag negative trends early. Build predictive models to forecast warranty exposure, anticipate emerging product issues, and support proactive decisions that improve customer experience and operational excellence. Set baselines and targets for quality KPIs in partnership with Quality and Operations leadership. Cross-functional collaboration and continuous improvement Partner with Engineering, Operations, and suppliers to investigate quality issues and support corrective and preventive actions (CAPA). Share insights from customer feedback and field returns with Product Development to inform future designs. Provide data-driven recommendations and follow up to measure whether implemented changes delivered the expected results. Qualifications: Required Bachelor's degree in Statistics, Mathematics, Engineering, Computer Science, or a related field. 3+ years of experience in data analytics, preferably in consumer goods, consumer electronics, or a related industry. Strong proficiency in SQL, plus experience with a scripting or programming language (such as Python or R) for data analysis. Hands-on experience with data visualization tools (such as Power BI or Tableau) and building dashboards for business users. Solid knowledge of statistical techniques and concepts (regression, probability distributions, hypothesis testing and their proper use) and experience applying them to real business problems. Experience working in a fulfillment, production, or other operational environment. Excellent analytical, problem-solving, and communication skills, with strong attention to detail. Preferred Familiarity with quality tools and methodologies such as Six Sigma, Statistical Process Control (SPC), 8D, or root cause analysis. Experience analyzing warranty, returns, or customer feedback data, including text analysis of reviews or service tickets. Experience with predictive modeling or machine learning techniques. Ability to manage multiple priorities in a fast-paced environment and to work independently. TURTLEBOX