Data Analyst Interview Questions for AI Training Work

I start by profiling the data for missing values, duplicates, and outliers, then decide case by case whether to impute, drop, or flag issues based on how they'd affect the specific analysis. I document every transformation so the cleaning process is reproducible and reviewable, not just a black box.

I check the distribution of the data and whether the groups are independent or paired first, since that determines whether a t-test, a non-parametric alternative like the Mann-Whitney U test, or a paired test is appropriate. Choosing a test based on convention rather than the data's actual properties leads to unreliable conclusions.

Common issues include truncated axes that exaggerate differences, using pie charts for data with too many categories to compare visually, or choosing a chart type that implies a trend the data doesn't actually support. I try to match the chart type to the specific comparison I want the viewer to make.

I investigate the source of the outlier before deciding how to handle it, since a data entry error should be corrected or removed, while a genuine extreme value, like a real high-value transaction, should usually stay in the dataset. Removing outliers automatically without checking their origin risks losing real signal.

I check whether the relationship holds across different time periods or subsets of the data, and I consider whether a plausible causal mechanism exists rather than just reporting the statistic. I'm also cautious about multiple comparisons, since testing many variables increases the chance of finding a spurious correlation by chance.

I'd run the analysis honestly and present what the data actually shows, even if it doesn't fully support their expectation, while framing the findings constructively. If the data is genuinely ambiguous, I'd say so directly rather than cherry-picking results to fit the requested narrative.

I'd spend a short amount of time doing exploratory analysis to understand the structure, ranges, and likely meaning of each field before trusting any of it, since undocumented data often has quirks like inconsistent units or placeholder values. I would rather flag ambiguity to the requester than guess and produce a wrong answer confidently.

I'd clarify what decisions the report needs to support before building it, since that determines which metrics matter and how often it needs to refresh. I'd also build in validation checks so a broken data source produces a visible alert rather than a silently wrong report reaching stakeholders.

A team was about to expand a product feature based on strong overall engagement numbers, but my analysis showed the growth was concentrated in a single user segment that wasn't representative of the broader base. Presenting that segmentation shifted the decision toward a smaller pilot instead of a full rollout.

I had a multi-variable regression result that explained a churn pattern, but the audience needed a decision, not a model summary. I translated it into a small number of clear, ranked factors with supporting visuals, and left the full statistical detail in an appendix for anyone who wanted to dig deeper.