Data Analyst
At ByRecruiters, I worked on analysing and improving the quality of recruitment and candidate data to support reporting and business insights. My role involved data cleaning, validation, and visualisation using Python to ensure data accuracy and usability.
I used Python (pandas, NumPy) to clean and prepare datasets by handling missing values, removing duplicate records, standardising inconsistent formats, and validating key attributes such as candidate profiles, job IDs, and application dates. I performed exploratory data analysis to identify anomalies and data quality issues that could impact reporting outcomes.
After preparing the data, I created data visualisations using Python libraries such as Matplotlib and Seaborn to present insights clearly to stakeholders. These visualisations highlighted trends such as candidate application volumes, recruitment funnel performance, and data quality improvements before and after cleansing. The visuals supported decision-making by making complex data easier to interpret.
I also documented the data cleaning logic, transformation steps, and visualisation outputs to ensure transparency and repeatability. My work improved data reliability, reduced reporting errors, and enabled the team to generate accurate, insight-driven reports.
This experience strengthened my ability to transform raw data into clean, visual, and actionable insights aligned with business needs.