Lab data & reporting analyst
Here's what AI can do for this role — and what still needs a human. Built straight from ENSEK's own job advert, running live on my_db.fisher_demo.clinical_samples. Not a slide about AI. The job, getting done.
Every line on the left is lifted from ENSEK's actual job ad. If a card lacks a harvested JD line, it is omitted. On the right is the AI doing it — with eligible cards running live against the warehouse and offline inspection clearly labelled in the workspace.
“segment sample throughput by type and site to target quality improvement campaigns.”
How does sample volume break down by type and site — where are the throughput hotspots and which combinations are underrepresented?
bar chart“produce trusted quality reporting that informs GxP corrective actions and panel-level investigation.”
How does out-of-range rate vary by test panel and collection month — where are the quality hotspots the lab needs to investigate?
table“batch quality reporting for GxP investigation and corrective action.”
What is the batch pass rate across studies — and which batches have high rejection or retest rates, signalling a systemic issue?
table“study completion velocity — samples received vs protocol target, used by the CRA team for site management.”
How is sample collection progressing per study — what proportion of samples are in Final status and which studies have large Pending backlogs?
kpi“GxP compliance audit gap detection — audit_complete=false where gxp_compliant=true, the 21 CFR Part 11 data integrity exception list.”
How many samples have a GxP compliance flag but an incomplete audit trail — the 21 CFR Part 11 data integrity gap the QA team must close?
deviationThe honest other half. AI does the analysis; a person owns the decision — especially where regulation, fairness and accountability bite.
A plain-English question — the same one the job ad describes — is translated to SQL by the agentic backend.
Curated cards run server-side against MotherDuck when eligible. The workspace separately labels any local inspection path.
Runs against my_db.fisher_demo.clinical_samples. No synthetic numbers.
Each figure carries a falsifier — recomputed from the result set, not a stored number, so it can't quietly drift.
It's the role getting done: curated questions run live server-side against the warehouse; local inspection is labelled inside the workspace.
Open the live workspace →Provenance. Offline path: SYNTHESISED labelled 2,000-row clinical sample dataset (ITS-169, seed 20260610). Schema mirrors my_db.fisher_demo.clinical_samples. GxP-transparent: audit trail and data integrity markers per 21 CFR Part 11 / EU Annex 11. NOT real patient or sample data. Live path (dormant): server-side MotherDuck query against the full fisher_demo.clinical_samples table.
It's Sorted — I took ENSEK's job ads and didn't write a report on what AI could do. I built it. Get the rest sorted →
I'm trained on this proof and the real ENSEK: the Ignition meter-to-cash platform (seven modules), the move under Centrica in 2024, 7M+ energy accounts migrated for suppliers like British Gas and Utility Warehouse, and the Ofgem framing. Ask me how the Data Analyst function changes shape, or which open roles map to which Ignition module.