Finance Analyst (AI)

Location: United Kingdom; remote

Salary: Up to £84,000 per year, or equivalent in local currency

Company

hirify.global is rebuilding energy transaction infrastructure with AI-native technology designed to make electricity markets more transparent, efficient, and fair.

What you will do

• Analyze performance and forecast variances to identify drivers and improve forecasting.
• Build analysis that shows the business levers available to improve outcomes.
• Own data inputs for recurring financial performance reporting, dashboards, presentations, and ad hoc analysis.
• Verify finance dashboard logic and ensure reporting outputs are accurate.
• Align finance reporting with underlying data definitions and resolve open questions such as P442 revenue.
• Run decision-ready analysis for the CFO, VP of Financial Performance, Head of FP&A, Global Controller, and wider Finance team.

Requirements

• 2–3 years of experience in investment banking, financial modelling or data analysis at a Big Four firm, or a top-tier consultancy.
• Strong Excel skills and working knowledge of SQL.
• Strong analytical judgment and the ability to turn ambiguous finance questions into structured analysis.
• Clear communication skills, with outputs usable by non-technical stakeholders.
• Ability to work as Finance’s day-to-day counterpart to the data team.

Nice to have

• Exposure to energy, fintech, or trading businesses.
• Experience with BI or dashboard tools.
• Experience working directly alongside a data team.

Culture & Benefits

• Remote-first and flexible working with clear core hours.
• No internal meetings on Friday afternoons.
• 25 days of holiday plus public holidays, with the option to swap public holidays and take the birthday off.
• Up to £1,200 or €1,200 per year for remote-work equipment.
• Up to £150 or €150 per month for wellbeing.
• Stock options and competitive compensation.

Hiring process

• Initial call with the Talent Team.
• Behavioral interview with the Hiring Manager, followed by a 90-minute technical interview involving a practical dataset analysis exercise.
• Culture-add interview with stakeholders.