Hiring nowRetail, ecommerce, advertising and sales intelligence

Advertising AI Scientist

Also called Ads ML Scientist

An Advertising AI Scientist builds and tests models that decide which ads people see, within privacy and policy limits. They show whether advertising spend creates real extra value, not just activity that would have happened anyway.

What they do

They build ad ranking and relevance models and work out how to measure what those models actually cause. Day to day, that means designing experiments, checking whether results hold up, and tracing odd movements in performance back to their source. They work with product, engineering, legal and commercial teams to agree what data can be used and with what consent. They test models for unfair outcomes across groups of users or advertisers. They write up findings in plain language so commercial leaders can act on them. They also retire models that look good in offline tests but do not hold up in live experiments.

What a strong hire delivers

In the first months, a strong hire gives the company a trusted way to tell whether an ad model adds value. They deliver a working ranking or relevance model with an experiment plan behind it. They set out clear consent and fairness checks that the team applies before launch. They also leave commercial colleagues with reporting they understand and believe.

How you can tell they are good

Ask them to walk through a past ad or recommendation model from problem to live test. Good candidates explain how they separated true incremental effect from correlation, and what they did when an experiment disagreed with offline metrics. They should talk comfortably about consent limits and fairness, with examples of choices they made and what those choices cost.

Why it is hard to fill

The title varies widely, and the work sits between machine learning, causal measurement and commercial judgement. Many applied ML people have never run a clean experiment. Many marketing scientists have never built a ranking system. Privacy and fairness experience adds a third filter. Candidates who cover all of it are few, so we usually search across adjacent backgrounds and judge the work they have done.

Where to find them

Look in ecommerce, marketplaces, retail media and ad technology teams. The titles are often applied scientist, marketing scientist, data scientist in growth or search, or ML engineer on recommendations. Strong people also come from commercial analytics and econometrics, especially those who have moved towards modelling and experimentation. Consumer platforms with large advertiser bases are a good source.

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 Advertising AI Scientist

  1. Tell me about an ad or recommendation model you took live. How did you decide it was working?

    Strong answers describe the objective, the baseline, the experiment design and the result in business terms. They mention surprises and trade-offs. Worry if they only quote offline metrics or cannot say what the model changed for users or advertisers.

  2. How would you measure the incremental value of a campaign when you cannot run a full randomised test?

    Good answers cover options such as geographic tests, holdouts, matched comparisons or instrumental approaches, and the assumptions each needs. They state limits honestly. Worry if they treat attribution reports as proof of cause.

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

Job description you can post

We are looking for an Advertising AI Scientist to build and evaluate models that decide which ads people see. You will work with product, engineering and commercial teams to show real incremental value, while respecting user consent, privacy rules and fairness standards.

What you will do

  • Build and improve ad ranking and relevance models
  • Design and run experiments to measure incremental value
  • Agree evaluation methods with product and commercial teams
  • Test models for unfair outcomes and report the findings
  • Work within consent, privacy and policy limits on data use
  • Investigate gaps between offline and live results
  • Explain results clearly to non-technical colleagues

What you will bring

  • Shipped a ranking, recommendation or ad model into live use
  • Run causal tests and can explain their limits
  • Strong grounding in probability and statistics
  • Experience with search or recommendation systems
  • Has worked with consent or privacy constraints on data
  • Can connect model results to commercial outcomes

Nice to have

  • Experience with CRM and customer data
  • Background in marketing science or econometrics
  • Experience assessing fairness or impact of models

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

Skills that matter

  • Information retrievalAdvancedRetrieve and rank relevant documents or entities
  • Probability and statisticsAdvancedQuantify variability and statistical evidence
  • Commercial and financial analysisWorkingAssess unit economics and value realisation
  • Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts

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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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