Data Analyst — Energy Retail (Oil & Gas)
- Compensation
- Not disclosed
- Location
- London, Greater London, United Kingdom
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Data analytics
- Posted
Skills
Must have
- 4+ years as a data analyst or in a comparable analytical role, owning your own analyses from question to recommendation
- Experience with high-volume B2C transactional data — prepaid mobile or telecoms, mobile money, utilities, pay-as-you-go energy, retail or similar. You know what subscriber-, account- or transaction-le
- Commercial analytics in practice: churn and retention, cohort and lifetime-value analysis, segmentation, pricing or campaign evaluation
- Strong SQL and Python (pandas or similar), and readiness to work in raw data yourself: unfamiliar schemas, undocumented tables, exports never meant for analysis
- Sound statistics for business questions — baselines, control groups, seasonality, telling a real effect from noise
- Working with coding agents, seriously. Agents write most of the code here. Bad agent output in analysis fails silently: a join fans out rows, a filter drops half the active customers, a top-up is attr
- Experience with a BI tool (Power BI, Tableau, Metabase, Superset or similar)
- Clear communication: you can explain a result, its limits and what to do about it to a non-technical manager
- English sufficient to present findings and defend methodology
- Professional proficiency in Russian
Nice to have
- Experience in emerging markets or with mobile money data
- Experience with IoT, smart-meter or device data
- Field operations or logistics analytics — routing, depots, delivery efficiency
- Experience with experimentation or A/B testing at scale
Full description
About the role
A hands-on analyst for a consumer energy business: pay-as-you-go cooking gas (LPG) delivered to hundreds of thousands of households in fast-growing emerging markets. You own the analysis end to end — framing the question, getting the data, building the numbers and defending them in front of the people who will act on them. You work directly with commercial, operations, finance and management, and with our founders — no account manager in between.
You join our energy data team, alongside a senior data scientist and a data & BI engineer. A meaningful part of the work starts in raw tables, meter logs, payment exports and field-operations files rather than in a tidy warehouse.
We do not require you to come from oil & gas. The business model is much closer to a prepaid mobile operator than to a fuel station: customers top up credit with mobile money, consume in small amounts, and every unit used is recorded by a smart meter. Experience at mobile network operators, telecoms, mobile money or fintech, utilities with prepaid metering, pay-as-you-go solar, and similar high-volume B2C businesses is directly relevant.
What the work looks like
Each customer home has a stove, a smart meter and a gas cylinder owned and maintained by the company. Customers pay only for the gas they use, topping up as little as a single meal’s worth through mobile money, and local technicians replace cylinders before they run out. That produces rich data — consumption, top-ups, cylinder swaps, deliveries, meter events — and commercial questions to match: which customers stay and why, what a customer is worth, where the network makes or loses money, and what to do about it.
Scope
- Customer analytics — acquisition, activation, retention and churn, cohort behaviour, revenue and margin per household, lifetime value against the cost of installation
- Consumption and top-up behaviour — frequency and size of top-ups, usage patterns, seasonality, early signs that a customer is about to go dormant
- Pricing and promotion analysis — the effect of price, top-up incentives and campaigns on usage and margin, measured against proper baselines and control groups
- Field operations and unit economics — cylinder swap and delivery efficiency, depot and area performance, technician productivity, cost to serve per household
- Forecasting gas demand, cylinder logistics and revenue by area, to support supply, stock and expansion decisions
- Anomaly and fraud detection — meter tampering, consumption that does not match cylinder swaps, payment irregularities, unusual field activity
- Reporting with single agreed definitions where commercial, operations and finance count customers, revenue or volume differently