Head of Product — Platform · 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 — 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 deliver clear OKRs in partnership with engineering and commercial leaders.”
What is the platform book — active account count, meter count and total consumption volume across all teams?
kpi“Treat internal engineering teams as primary customers, shaping platform priorities based on their needs and workflows.”
What is the fuel and settlement mix — dual fuel vs single fuel and HH vs NHH — the complexity profile the platform must serve?
bar chart“Define and own the Platform product strategy and roadmap, balancing near-term delivery with long-term architecture.”
Where is churn risk concentrated — high-risk accounts by customer segment and region, the retention signal for leadership?
bar chart“Drive evidence-based prioritisation, making clear decisions on what to improve, replace, or retire.”
What is the revenue at risk from arrears — balance distribution among in-arrears accounts for board reporting?
bar chart“Own ENSEK’s AI platform strategy, enabling safe, scalable adoption of agentic AI and productivity tooling across product and engineering teams.”
What is the platform's carbon and retrofit profile — total CO2 and retrofit gap by customer segment, the net-zero contribution signal?
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 (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. Representative ENSEK-style operational dataset (480 accounts · 728 meters · 5,676 monthly consumption records). Schema mirrors my_db.ensek_demo.accounts/meters/consumption_monthly. Seed 20260609 — reproducible. No real ENSEK or customer data. Live server-side path: my_db.ensek_demo.accounts (27.6M rows). Dormant until operator provisions MOTHERDUCK_TOKEN.
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.