Head of BI

• Own and fix Nous’s business intelligence after 20x growth in 12 months across energy, mobile, broadband, insurance, and subscriptions left reporting behind. • Set the standards rather than inherit them, with no legacy team, no committee, and no untouchable five-year-old semantic layer. • Take unit economics from decent estimates to a genuinely defensible view of LTV, payback, and cohort behaviour, on a problem with no textbook answer. • Own the data foundations: canonical metric definitions, the dbt models underneath them, and the reporting Growth, Commercial, Operations, and Finance depend on. • Work directly with the senior team including the CEO, Head of Finance, and the Growth, Commercial, Operations, and Product leads. • Produce the work the board sees. • Work with a stack of Postgres replicated into Snowflake via Fivetran, dbt for transformation, Omni for modelling and dashboards, Amplitude for product analytics, and Statsig for experimentation. • Stay hands-on, writing the SQL and building the models yourself for a good while, since Nous keeps the middle thin on purpose. • Work office-first from Farringdon, with pragmatism about heads-down work.

• Bring substantial experience in analytics, BI, or data leadership, including a spell owning the numbers for a fast-growing consumer or transactional business. • Bring confident SQL, getting to a defensible answer from an unfamiliar schema without help. • Work hands-on with a modern warehouse such as Snowflake, BigQuery, or Redshift, and a transformation layer such as dbt, having written and maintained the models yourself. • Work hands-on with a BI and semantic modelling tool such as Omni, Looker, Lightdash, Metabase, Power BI, or Tableau, including designing the semantic layer rather than only building charts on it. • Bring event analytics experience with Amplitude, Mixpanel, GA4, or similar, and understand where event data and database data disagree and why. • Show a track record of establishing metric definitions and getting an organisation to adopt them, which is as much persuasion as technical work. • Work comfortably with finance on management reporting and reconciliation. • Use AI tooling actively in your day-to-day work, such as Claude Code for writing and debugging SQL, navigating unfamiliar schemas, and automating your own checks. • Be commercially minded as well as technically strong, with a view on what the model says and what to do about it. • Be fussy about correctness to a degree others find excessive, triangulating and reconciling rather than shipping a chart you cannot defend. • Push back on numbers senior people have grown attached to, having already checked them three ways. • Simplify, working out which six dashboards matter and killing the rest. • Work office-first from Farringdon.

• Bring experience in consumer subscription, marketplace, fintech, energy, or utilities, or anywhere with messy third-party data and revenue that is not a clean row in a table. • Bring experience modelling LTV, payback, and cohort behaviour where inputs are commercial assumptions rather than observed values. • Bring experience with experimentation platforms such as Statsig, Optimizely, or GrowthBook, and read experiment results honestly. • Bring experience with board and investor reporting. • Bring experience with instrumentation and tracking plan design, including working with engineers to get events fired in the right places. • Bring some Python for the analysis that does not fit in SQL. • Have built your own tools when the available ones were not good enough.

• Own the canonical definitions, sponsoring an agreed definition for each fundamental metric and holding the line when someone wants a subtly different version for their deck. • Own the dbt and Omni models underneath, consolidating them, moving everything onto the current event standard, and retiring what is no longer earning its place. • Make the numbers provable, building the reconciliation and automated checks that keep events, database tables, and the finance ledger agreeing as the business scales. • Make self-serve work properly through a clean semantic layer, sensible naming, documentation people actually read, and a small set of trusted dashboards. • Own commercial reporting and unit economics including revenue per switch by product and supplier, contribution margin, CAC and payback by channel, cohort retention, repeat switching, and LTV. • Partner with Finance on management reporting, the board pack, and the reporting infrastructure behind investor updates. • Act as the analytical partner to commercial teams, answering their questions and then building the reporting that stops them being asked again. • Set governance covering access, PII handling, how deleted users are treated in analytics, and what can leave an aggregate. • Own the commercial side of the data stack, since pipeline spend at this scale is worth owning properly. • Continue driving the use of AI, since most analytical work now happens in Claude Code alongside SQL.

• Receive a compelling package including share options with life-changing upside. • Take 33 days paid holiday including bank holidays. • Get the equipment, setup, and training you need to do your best work. • Work in a high-context environment with real exposure to the big decisions. • Build the BI function from scratch with no legacy team or committee to work around. • Work in a high-performance, high-agency, high-trust technical environment that is intentionally lean and AI-native. • Use a great office set up for collaborative working, with very good coffee and a well-stocked kitchen. • Access health and dental cash plans plus an employee assistance programme. • Use travel benefits including the cycle-to-work scheme and season ticket loans. • Join an informal social every week, something bigger monthly, and set-piece events in summer and at Christmas.

Nous is an AI agent that takes some of the load of life, making good decisions and acting for users in areas where they are not the experts or are not paying enough attention. It started with optimising bills, where it already saves households thousands, and is expanding into new categories. The company is growing 20x year on year with an NPS of +70, and has launched insurance, where it is the first with regulatory approval to run the full discovery-and-purchase journey as an agent, and subscription management using Open Banking to actively cancel unwanted subscriptions. Its founding team has multiple exits and its investors include the founders of Monzo, Wise, Booking.com, lastminute.com, Onfido, Funding Circle, Tide and Habito.