Global shared-services financial 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.financial_periods. 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.
“P&L by division and region — the primary IBCS-standard segment view the FP&A Manager publishes at every close cycle.”
How does gross margin vary by division and region — which combinations are delivering the highest margin contribution and where is the pressure?
bar chart“budget variance waterfall — track actuals vs budget by division and quarter for the close cycle.”
Which cost centres are materially over or under budget — and which divisions show the widest variance requiring investigation?
table“organic vs acquired growth split — the three-statement model isolation that separates underlying business performance from M&A activity.”
What is the organic growth trend across divisions — and which divisions are growing through M&A versus organic performance?
bar chart“headcount efficiency — revenue per head by division and region, the shared-services efficiency KPI.”
What is the revenue-per-head efficiency by division and region — where is headcount generating the highest and lowest revenue return?
table“forecast vs actual trend — track forecasting model accuracy over time to measure FP&A process maturity.”
How has the forecast accuracy (budget variance) trended over time by division — is the forecasting model improving or drifting?
tableThe 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.financial_periods. 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 1,200-row financial periods dataset (ITS-169, seed 20260612). Revenue proportions CALCULATED from Thermo Fisher Scientific public annual reports (2022-2025). Division splits approximate TFS reported segment mix. NOT real TFS financial data. Live path (dormant): server-side MotherDuck query against fisher_demo.financial_periods.
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.