Role Description The Data Quality Analyst at Togo AI Lab is a part-time, remote role responsible for ensuring the accuracy, completeness, and consistency of data used in AI research and products. On a day-to-day basis, this role involves reviewing datasets for anomalies, validating data against defined standards, and documenting data quality issues for the technical team. The analyst will collaborate with data engineers and data scientists to define quality metrics, design quality checks, and support the improvement of data collection and transformation processes. Typical tasks include performing data profiling, running validation queries, maintaining data dictionaries and quality reports, and communicating findings clearly to stakeholders. The role also contributes to continuous process improvement by recommending best practices for data governance, quality monitoring, and documentation. Qualifications Demonstrated experience in data analysis and data validation, including the ability to identify patterns, anomalies, and inconsistencies in structured and unstructured datasets. Proficiency with data tools such as spreadsheets, SQL, or basic scripting (e.g., Python or R) for data profiling, quality checks, and reporting. Strong analytical thinking, attention to detail, and problem-solving skills, with the ability to translate data quality findings into clear, actionable recommendations. Effective written and verbal communication skills for documenting data issues, preparing quality reports, and collaborating with technical and non-technical team members. Ability to work independently in a remote, part-time setting, manage multiple tasks, and meet deadlines with minimal supervision. Understanding of basic data governance concepts, data privacy considerations, and ethical use of data in AI applications. Bachelor’s degree or equivalent experience in a quantitative field such as Data Science, Statistics, Computer Science, Information Systems, or a related discipline. Prior experience in AI, machine learning, or research environments is beneficial but not required; interest in learning about AI and data-centric practices is highly valued.