About the job
We are looking for a meticulous and technologically adept Market Research Data Analyst to become a vital part of our Research Operations team. In this pivotal position, your primary responsibility will be to handle and refine raw survey and behavioral data, transforming it into organized, structured formats suitable for analysis. This encompasses quality assurance, coding, weighting, dataset merging, and guaranteeing the precise delivery of final data files to both research teams and clients. Your role is crucial in maintaining the integrity and reliability of our research findings.
Key Responsibilities:
- Data Cleaning & Quality Control: Perform comprehensive cleaning of raw datasets by identifying and correcting data discrepancies, eliminating duplicates, addressing missing values, and ensuring data consistency from various survey platforms (e.g., Qualtrics, Alchemer) while preparing them for analysis using tools such as Excel, SPSS, or R.
- Data Transformation: Standardize and reformat raw data to align with research objectives and client specifications.
- Tabulation & Aggregation: Develop frequency tables, cross-tabulations, and summary statistics to bolster insights reporting and client presentations.
- Weighting & Tabulations: Implement weights on datasets and produce frequency tables and cross-tabulations as necessary.
- Data Integration: Merge data from various sources (e.g., surveys, tracking studies, spreadsheets) into cohesive datasets for analysis and reporting.
- Export & Documentation: Generate clean, well-annotated datasets and export them into designated formats (e.g., Excel, CSV, SPSS) along with necessary documentation.
- Quality Control: Execute systematic checks to validate data accuracy and completeness before delivery.
- Collaboration: Partner closely with research analysts, project managers, and survey programmers to ensure timely and accurate data delivery.
- Coding of Open-Ended Responses: Categorize qualitative text responses manually or semi-automatically using established coding frameworks to enable quantitative analysis.
- Automation & Scripting: Utilize scripting languages or tools to automate repetitive tasks (e.g., Python, R, Excel macros).
