Quality Data Analyst

Turtlebox Audio•Houston, TX

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.