Report quarter-end headcount and attrition from an effective-dated HR export with corrections, rehires, future-dated leavers and contractors

# Quarter-end headcount and attrition from the HR system export Inputs: `job_history.csv` (one row per change to a worker's record: each row is the full state of the record from its effective date) and `request.json` (the reporting date and window). Report headcount and FTE by department as of the reporting date, and voluntary attrition over the trailing 12 months. - A record's state on a date is its row with the latest effective date on or before that date. Rows effective after that date haven't happened yet. - When two rows share an employee and an effective date, the one entered later is a correction and replaces the other. - Count people, not employee ids: someone who left and came back has a new employee id but the same person id. Contractors are not headcount. - Voluntary attrition = voluntary leavers in the window / average of the headcount at the window's start date and at the reporting date x 100. A leaver is a person whose employee record changes to terminated with a voluntary reason and an effective date inside the window (after the start date, on or before the reporting date). A person who left and was rehired within the window still counts as a leaver. Write `out/headcount.json`: `{"headcount_by_dept": {"<dept>": <n>, ...}, "fte_by_dept": {"<dept>": <sum, 2 decimals>, ...}, "total_headcount": <n>, "voluntary_leavers": <n>, "voluntary_attrition_pct": <2 decimals half-up>}`. Leave out departments with no headcount. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.