Data Research Analyst

Role & responsibilities

Analyze structured and unstructured survey data to extract trends, patterns, and key insights related to customer experience, satisfaction, and behavior.

• Review and interpret open-ended (verbatim) responses, identify key themes, and assess sentiment using manual and automated methods.
• Support Quantitative Research and VoC/CX programs by creating cross-tab reports, dashboards, and insights summaries.
• Work closely with Consumer Insights, Marketing, Product, and Experience teams to translate data into business narratives.
• Design and deliver visually compelling PowerPoint presentations highlighting findings, pain points, and strategic recommendations.
• Present insights to senior stakeholders in a clear, confident, and consultative manner.
• Collaborate with stakeholders to understand business questions and align analysis to their objectives.
• Maintain quality standards in data cleaning, validation, and integrity across survey datasets.
• Continuously explore new methods, tools, and frameworks to improve insight generation.
• Use statistical techniques to support advanced analysis, including regression, factor analysis, or segmentation.

Preferred candidate profile

Bachelors or Master’s degree in Statistics, Economics, Market Research, Business Analytics, Psychology, or related fields.

• 7+ years of experience in Quantitative Research, Customer Insights, VoC programs, or CX analytics.
• Strong analytical skills and comfort with large datasets derived from surveys or feedback platforms (e.g., Qualtrics, Medallia, SurveyMonkey, etc.).
• Proven ability to interpret data and translate it into insightful, business-relevant stories.
• Familiarity with open-end verbatim analysis, sentiment tagging, and theme identification.
• Excellent skills in Microsoft PowerPoint – ability to create compelling insights decks.
• Proficient in Excel or Google Sheets for data analysis and tabulations.
• Strong presentation and communication skills – must be able to deliver insights to leadership-level stakeholders.
• High attention to detail and commitment to accuracy and data quality.
• Knowledge of basic statistics or tools like SPSS, R, or Python