Payments, arrears & refunds PM · vacancy
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. 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 the strategy, vision and roadmap for payment and refund processing — engaging users, stakeholders and commercial partners to define, validate and iterate it continuously, using competitive intelligence and AI tools to inform positioning.”
What's the shape of the arrears book — how many accounts, what segments, and what's the revenue at risk?
table“Drive internationalisation of payment capabilities, ensuring the platform supports compliant payment processing across each regulatory jurisdiction without bespoke builds.”
How is the book balanced — what share of active accounts are in credit versus in debt, and by how much?
bar chart“Define and track key product outcomes — implementing dashboards for real-time performance visibility, running A/B and multivariate test and learn, and using data to drive continuous improvement in quality and user experience.”
Which active accounts have credit balances large enough for a refund review — the refund queue?
deviation“Create and maintain a prioritised roadmap, working with your team to validate what adds value now and in future, drafting PRDs and specifications.”
Where do arrears and churn risk concentrate together — the accounts most at risk of self-disconnecting before intervention?
table“Build long-term stakeholder relationships, implementing communications strategies and influencing effectively to drive alignment and outcomes.”
Where do arrears concentrate by tariff type and region — which products and geographies drive the debt book?
bar chartThe 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. 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. Representative ENSEK-style operational dataset (480 accounts · 424 active · 54 in arrears). Schema mirrors my_db.ensek_demo.accounts. Seed 20260609 — reproducible. No real ENSEK or customer data.
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.