Energy retail debt management PM
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.ensek_demo.accounts — 27,640,145 real rows via MotherDuck (DuckDB). 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.
“Own and evolve the credit risk product strategy, vision and roadmap across the full customer lifecycle”
Show the debt book aged by time in arrears — balance and account count by aging bucket.
bar chart“Define and prioritise initiatives spanning acquisition, in-life risk management and collections strategies”
Where is the uncovered balance — which aging bands have the highest share of debtors with no active payment arrangement?
bar chart“Translate complex regulatory requirements (FCA, Ofgem, ICO) into clear, actionable product deliverables”
Show the payment-plan adherence backlog — arrangements with broken promises, ranked by missed installments.
deviation“Partner with engineering to scope, size and deliver scalable, data-driven capabilities”
Show the recovery funnel by stage — how many accounts and what balance are at each escalation stage?
bar chart“Use data to define KPIs, dashboards and performance frameworks, driving continuous improvement”
What is the write-off exposure by reason — how much has been written off, and where is it concentrated?
kpi“Collaborate with clients and stakeholders to ensure the product addresses real-world credit and collections challenges”
How does the debt journey differ for PSR-registered customers — are we treating vulnerable customers differently from standard?
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.ensek_demo.accounts (27,640,145 rows declared by the manifest). 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. Synthetic ENSEK-style credit-risk dataset (380 accounts · 253 debtors · 107 payment arrangements · 290 installments · 271 recovery events · 23 write-offs). Reproducible — generated by gen-credit-risk-data.mjs (seed 20260609). No real ENSEK or customer data. 'Today' is day 160 (2026-06-10); all aging, coverage, adherence and exposure metrics re-derive against it inside each query. Live server-side path: my_db.ensek_de
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.