Metrics with consistent definitions.
Make dashboards easier to interpret responsibly.
Where this work fits
Once AI features were in use, a disappointing result could come from several places. A model call, a search result, and a dashboard count each needed different evidence before the team could decide what to fix.
I worked on SQL reporting for candidate activity, including the populations and time periods behind the numbers. I separated measures of different actions and adjusted comparisons so incomplete periods did not make a rate look worse simply because the day was still underway.
Understand what the measurement counts
The denominator matters.
Different questions deserve different measures.
40 applications submitted in total.
This total counts submissions. It does not tell us how many people started an application and finished it.
Capture the right context without blocking the user.
The review team needed the result the user actually saw. I worked on carrying that context into the review handoff while keeping a delivery failure from interrupting search. For metrics, I checked what each count represented and kept incomplete periods from distorting comparisons.
The skills behind the work
- SQL
- Query and update relational data, with clear rules about which records a result represents.
- Metric definitions
- Make sure a report’s label and the action being counted mean the same thing.
- Product measurement
- Connect user behavior with a product question rather than count events without context.
Engineers could inspect model failures and timing. Reviewers received search context to investigate, and product reporting used clearer definitions for the activity being measured.