Hiring nowData science, analytics and decision support

AI Business Intelligence Analyst

Also called AI BI Analyst

An AI Business Intelligence Analyst builds reporting the business can trust on workflows that use AI. They show leaders what is working, what is not, and which numbers to believe before decisions get made.

What they do

They build and maintain dashboards that track AI-enabled workflows, such as support triage, document processing or sales assistance. They pull data with SQL, clean and reconcile it in Python or R, and check it against finance, operations and product sources. They define each metric in writing, including what it counts and what it leaves out. They look into odd movements, work out whether the cause is real or a data fault, and explain it to non-technical colleagues. They also help design simple tests of whether an AI feature is making a difference.

What a strong hire delivers

In the first months, a strong hire gives the business dashboards that agree with the finance and operations numbers. They write metric documentation that people actually use. They find and fix at least some long-standing data disputes. Leaders start to ask what a number means and what to do about it, rather than whether it is right. They also set up a routine for checking reports as AI workflows change.

How you can tell they are good

Ask them to walk through a dashboard they built and how they knew it was right. A strong candidate talks about reconciliation, definitions and edge cases before charts. They can explain a metric that misled people and how they caught it. Give them a messy dataset and a vague question. Good ones clarify the decision first. Be wary of polish with no account of where the numbers came from.

Why it is hard to fill

Titles are the problem. The same work sits under BI analyst, data analyst, analytics engineer and reporting lead, and few have reported on AI workflows. Many strong analysts have only done standard business reporting. Many data scientists would rather model than reconcile. The mix needed is sound statistics, careful reporting habits and comfort with AI outputs that are uncertain. That combination is uncommon, so we search across adjacent backgrounds.

Where to find them

Most come from business intelligence, analytics, data analysis or applied statistics roles. Look in product analytics teams, finance and operations analytics, and consultancies that run reporting programmes. Some are data scientists who prefer decision support to model building. Others come from research or experimental design backgrounds. Scale-ups and AI product companies that already track AI usage and quality are a good source, as are larger firms adopting AI inside existing operations.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Business Intelligence Analyst

  1. Tell us about a dashboard you built that people trusted. What did you do to earn that trust?

    Look for reconciliation against source systems, written metric definitions, agreed owners and a way to handle disputes. Worry if trust came from design or speed alone, or if they cannot say how they checked accuracy.

  2. Two sources give different totals for the same metric. How do you work out which is right?

    A strong answer traces each figure back to its source, compares definitions, filters and timing, and tests samples. They document the outcome. Worry if they pick the source they prefer or average the two.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for an AI Business Intelligence Analyst to build reporting our teams can rely on. You will work on AI-enabled workflows across the business, making sure the numbers are right, clearly defined and useful for decisions.

What you will do

  • Build and maintain dashboards covering AI-enabled workflows
  • Reconcile reporting against finance, operations and product data sources
  • Write and maintain clear metric definitions and documentation
  • Investigate unexpected changes and separate real shifts from data faults
  • Help design tests of whether AI features improve outcomes
  • Explain findings and recommendations to non-technical colleagues

What you will bring

  • Demonstrated reporting work that colleagues trusted and used
  • Strong SQL and working Python or R for cleaning and analysis
  • Solid grounding in probability, statistics and experimental design
  • Experience reconciling data from several sources
  • Clear written documentation of metrics and assumptions
  • Ability to turn a vague question into a measurable one

Nice to have

  • Experience reporting on AI or machine learning outputs
  • Familiarity with scikit-learn
  • Experience with a range of visualisation tools

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Probability and statisticsAdvancedQuantify variability and statistical evidence
  • SQLAdvancedQuery and validate relational data
  • PythonWorkingDevelop tested numerical and data software
  • Experimental designWorkingChoose comparisons that identify credible effects

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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