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About the role
A senior individual contributor owning the data science work end to end: deciding what to measure, getting the data, building the models, defending the conclusions. You work directly with traders, deal economists, finance and treasury, and the founders — no account manager in between.
We are hiring for a sector, not for one narrow problem. Assignments come from business stakeholders and they vary; what stays constant is the industry and the independence expected. The team can grow with one or two junior data scientists reporting to you.
There is a data engineer and an established data lake, but not every source is organised that way — a meaningful part of the work still starts in raw tables and unprocessed exports.
What the work looks like
The system we are building exists so that anyone involved in a deal can reconstruct the P&L between any two points of the chain, at any moment, on an agreed and versioned allocation basis. That is where the role sits: P&L, cost and financial transactions. Operational data is an input to margin, not a subject of its own.
This is operational deal P&L, reconciled to but not identical with the statutory accounts; the general ledger and month-close stay in the accounting system.
Scope
P&L reconstruction and margin attribution across the deal lifecycle — netback at entry, provisional P&L, final P&L after final pricing, actual costs, demurrage and claims — with a defensible account of every delta, and the ability to reproduce the P&L as of a past date
Forecasting where it pays on the financial side: provisional cost lines invoiced later, cost and margin per tonne, demurrage and claim exposure, FX and open pricing-period exposure, working capital and cashflow
Anomaly and fraud detection on financial data: invoice lines inconsistent with contract terms, cost booked to the wrong deal or period, duplicate invoices, off-market pricing between related parties, cost reallocation that improves one deal at another's expense
The reporting layer for finance and risk — margin, cost per tonne, exposure, receivables, cash position — with single agreed definitions where trading, economics and finance disagree
Turning vague business problems into a research plan, and results into a recommendation someone can act on Incomplete and contradictory financial data as the normal case: provisional costs never trued up, invoices arriving months late, deals kept open for years by claims
Requirements
7+ years in data science, with senior ownership of your own workstreams
Oil & gas industry experience — at least two years of delivered projects in trading, refining, logistics, storage or distribution. You can read a pricing clause, an invoice with its cost lines and a demurrage claim without a translator, and you know why provisional and final invoices for the same cargo legitimately differ. Which segment matters less. What matters is physical hydrocarbon volumes and index-linked pricing — power or utilities experience alone does not cover that
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 `dropna` removes half a population, a future-dated column leaks into features. We hire for verification habits, not speed — row-count and population checks, reconciliation against a number someone already trusts, held-out evaluation, a traceable path from raw data to any figure a decision-maker sees
Strong Python and SQL, and readiness to work in raw data yourself: unfamiliar schemas, undocumented tables, exports never meant for analysis
Forecasting and time-series modelling on real business data
Experience taking models into production and operating them; close to essential for us
Numbers that carry weight. Your output feeds invoices, hedges and shareholder reporting: reconciliation to the accounting system, a versioned and documented method, and an explanation the person signing the invoice can follow. Accuracy that cannot be explained loses to accuracy that can
English sufficient to present findings and defend methodology
Professional proficiency in Russian
What we offer
Ownership of the data science work, and room to shape how it is done
Direct access to founders and decision-makers; short feedback loops
Generous AI tooling budget — frontier models are a working expense, not a perk
Competitive compensation, discussed individually
How to apply
Share your CV an answer to any of the questions below, at your choice. Applications without answers are not reviewed.
A term contract drawn down over several lots was booked at a netback of about $14/t. The final P&L closes at $6/t and nobody can say where the rest went — pricing period, FX, provisional costs replaced by actuals, late demurrage and cost reallocation all have their advocates. How would you approach it, what would you need, and what would you deliver?
Give a real case where two systems or two departments reported different numbers for the same P&L, margin or quantity. What were the figures, why did they differ, which one did the business treat as truth, and what did you change?
Show a real prompt or task brief you gave an agent for an analysis or a model, and what it produced. Then one case where agent output looked right and was numerically wrong: what was wrong, how you caught it, what check you added.
Which systems have you personally pulled data from — CTRM/ETRM, ERP or accounting, invoice and settlement data, bank statements, terminal systems, price feeds? Pick one and tell us something about its data that only someone who has used it would know.