Data Analyst

Overview

TensorX is a sovereign AI infrastructure platform headquartered in Dublin. We run frontier open-weight large language models on our own NVIDIA Blackwell GPUs in European datacentres, under EU jurisdiction. Customers reach them through a drop-in OpenAI-compatible API. Nothing they send is retained after the request completes. We serve regulated enterprises in finance, healthcare and government as well as developers and AI platforms. We help them adopt AI without compromising on data privacy, compliance or performance.

We are looking for a Data Analyst to join our growing team. Reporting to the Finance Director, you will build a reliable picture of how TensorX performs, from revenue and token usage through to GPU capacity and operating costs. You will define the core metrics the business runs on and explain what drives changes in revenue and usage so the management team can plan and invest with confidence. This is a hands-on, cross-functional role with real scope to grow both your technical and commercial skills.

The Finance Director will set your priorities, and you will work closely with our technical and commercial teams every day. Our technical team will also help you learn our systems and review your GPU and infrastructure analysis. Much of your data will come from our databases and metrics systems (PostgreSQL, Prometheus and Grafana), and you will help establish the BI tools that the wider business needs. You will handle commercially sensitive usage and billing data in line with our information security controls and customer data commitments. This means token counts, request volumes and spend, but never the content that customers send.

TensorX is a founder-backed, execution-led business where people take ownership and work across functions. We are an AI-native team. Tools such as Claude Code and Codex are part of our daily workflow and materially accelerate how we build and operate systems. We value curious people who ask good questions and move easily between technical detail and commercial context.

Responsibilities

• KPIs & management reporting - Define and maintain the core KPIs with the management team and produce the monthly management reports, ensuring the numbers mean the same thing to everyone who uses them.

• Revenue & customer analysis - Analyse customer usage, signups, token consumption, pricing, revenue and profitability. Explain how changes in customer demand, model usage and token volumes affect revenue.

• Financial & operational analysis - Bring together financial, infrastructure and commercial data to show how the business performs. Build the analysis behind forecasts, financial plans and decisions on infrastructure investment and financing.

• Infrastructure costs & capacity - Analyse GPU capacity, utilisation, electricity costs (including European energy prices) and other operating costs. Feed the results into capacity planning and show where we can run more efficiently.

• GPU & inference performance - Analyse GPU utilisation, inference performance and workload efficiency. Track goodput per GPU, which is the number of requests each GPU answers inside the latency target. Run benchmarks with the GPU Performance Team's method and report how the results change over time. Work with engineers to investigate performance issues and measure the effect of each fix.

• Dashboards & automation - Help establish our BI tools and build reliable dashboards that act as a single source of truth for the business. Combine data from different systems and automate recurring analysis so that routine reports run without manual work.

• Business alerts - Build alerts on key business metrics, using the monitoring that our technical team already runs. Make sure we hear about a sudden change in revenue, usage or capacity before it becomes a problem.

• Investigation & problem solving - Take technical, operational and commercial questions from across the business and find the answers in the data. Explain what you find in plain terms and suggest practical improvements.

What Success Looks Like

Within your first 12 months you will have:

• Agreed the core KPIs with the management team and made your monthly report their trusted source of numbers

• Worked with the technical team to build one view of GPU utilisation and inference performance that the whole company uses

• Given the business clear evidence of what drives revenue and why usage growth and revenue growth can differ

• Made capacity, electricity and operating costs reliable inputs to capacity planning and the financial model

• Helped establish our BI tools, automated most recurring reporting and set up alerts on key business metrics

• Shared findings that led to a technical improvement, a commercial decision or a change to a planning assumption

Skills & Experience

• 3+ years of experience in data analysis, technical analytics, business intelligence or a similar role

• The ability to work independently and own reporting from end to end

• Strong quantitative and problem-solving skills. You can investigate unfamiliar datasets and explain what you find clearly

• Practical Python and SQL skills to extract, combine, analyse and check data

• Experience defining KPIs and building the dashboards and automated reports that other people rely on

• Confidence with revenue, cost, margin, utilisation and other business measures

• Some experience supporting forecasts, budgets or financial models

• A genuine interest in AI infrastructure and how it works as a business

• Comfortable using AI-assisted tools (e.g. Claude Code, Codex) as part of your daily workflow

• A clear communicator with engineers and non-technical colleagues. You know when to ask for specialist input

Nice to Have

• Experience in GPU computing, AI inference, cloud infrastructure or datacentre operations

• GPU and infrastructure monitoring tools such as nvidia-smi, DCGM, Prometheus or Grafana

• Experience with telemetry, time-series data or technical benchmarks

• Experience choosing or setting up BI and data tools such as Metabase, Power BI, Looker or dbt

• Linux, APIs and databases

• Product, web or billing analytics

• Pricing, unit economics or capacity forecasting

• European energy markets or datacentre economics

Education & Qualifications

• BSc/MSc in a quantitative discipline OR equivalent practical experience

Remuneration

• Highly competitive package, dependent on experience

• 25 days paid annual leave

• Hybrid working from our centrally located Dublin office, with remote flexibility

• Free inference tokens!

TensorX is an equal opportunities employer. We welcome applications from everyone and assess candidates on the skills and experience the role requires. Please let us know if you need any reasonable accommodation at any stage of the recruitment process.

******* NO AGENCY ASSISTANCE REQUIRED *******