Senior Data Analyst

Requirement

• Designing and executing exploratory analyses independently across new and complex datasets.
• Applying statistical concepts across univariate, bivariate, and multivariate analyses where appropriate.
• Understanding distributions, trends, relationships, correlations, outliers, anomalies, and behavioural patterns within data.
• Selecting analytical techniques based on the business problem rather than relying on predefined analysis templates.
• Performing comprehensive data validation and sanity checks before drawing conclusions.
• Assessing dataset granularity, completeness, uniqueness, consistency, coverage, and potential biases.
• Developing robust analytical logic to account for data gaps, null values, edge cases, and unusual scenarios.
• Combining multiple analytical perspectives or datasets where required to arrive at stronger conclusions.
• Identifying the most efficient way to investigate a problem and prioritising analyses based on likely business value.
• Using AI tools responsibly for coding, exploration, automation, documentation, and productivity improvements.