Analyst

Data Validation (DV) Specialist (Using SPSS) - Analyst

Job Description:

Core Responsibilities:

• Perform data quality checks and validation on market research datasets
• Develop and execute scripts and automated processes to identify data anomalies.
• Collaborate with the Survey Programming team to review survey questionnaires and make recommendations for efficient programming and an optimal layout that enhances user experience.
• Investigate and document data discrepancies, working with survey programming team/data collection vendors as needed.
• Create and maintain detailed data documentation and validation reports.
• Collaborate with Survey Programmers and internal project managers to understand data processing requirements and provide guidance on quality assurance best practices.
• Provide constructive feedback and suggestions for improving the quality of data, aiming to enhance overall survey quality.
• Automate data validation processes where possible to enhance efficiency and reduce time spent on repetitive data validation tasks.
• Maintain thorough documentation of findings and recommendations to ensure transparency and consistency in quality practices.
• Actively participate in team meetings to discuss project developments, quality issues, and improvement strategies, fostering a culture of continuous improvement.
  
  
  

• Bachelor’s degree in computer science, Information Technology, Statistics, or a related field.
• At least 2+ years of experience in data validation process.
• Familiar with data validation using SPSS, Dimension, Quantum platform or similar tools
• A proactive team player who thrives in a fast-paced environment and enjoys repetitive tasks that contribute to project excellence.
• Programming knowledge in a major programming language such as R, JavaScript, or Python, with an interest in building automation scripts for data validation.
• Excellent problem-solving skills and a willingness to learn innovative quality assurance methodologies.
• A desire for continuous improvement in processes, focusing on creating efficiencies that lead to scalable and high-quality data processing outcomes.