Hiring nowCausal inference, decisions and experimentation

Decision Scientist

Also called Applied Decision Scientist

A Decision Scientist turns uncertain evidence into clear, constrained choices. They help an AI company decide what to ship, test, fund or stop, and they check afterwards whether those choices paid off.

What they do

The work starts with a decision someone has to make, such as whether to ship a model change, set a threshold or spend more on evaluation. The Decision Scientist frames the options, the constraints and what a good outcome is worth. They build a decision model from the available evidence, say where it is weak, and work out which extra information would change the choice. They design experiments or observational analyses to get that information. They write down their assumptions, share the model with the people who own the decision, and plan how outcomes will be measured once the choice is made.

What a strong hire delivers

In the first months, a strong hire gets one or two live decisions framed in a way the team accepts. They deliver a decision model with assumptions stated openly, and an outcome measurement plan that is actually in use. They show which uncertainties are worth paying to reduce and which are not. Teams argue less about opinion and more about inputs, and past calls start to be reviewed against what happened.

How you can tell they are good

Ask them to walk through a real decision they shaped, from framing to outcome. Strong people state the alternatives, the constraints and what they assumed. They can explain what a piece of information was worth before they collected it. They tell you where they were wrong and how they found out. Be wary of candidates who describe only analyses and never the choice that followed.

Why it is hard to fill

The title is rare and the skills are spread across several disciplines. Many candidates are strong statisticians who stop at the estimate, or product analysts who run tests without modelling the decision. Few combine causal reasoning, decision theory and the nerve to put assumptions on the table. Titles vary widely, so searching by title alone misses most of the good people. Judging real depth takes a recruiter who can probe the work.

Where to find them

Look in experimentation and causal inference teams, applied economics, econometrics and quantitative research groups. Strong candidates also come from pricing, risk, marketplace and growth analytics, and from operations research. Typical titles include data scientist, economist, quantitative analyst and experimentation lead. Companies with large user bases, financial firms and model-driven product companies tend to produce people who have owned decisions with real consequences.

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 a Decision Scientist

  1. Tell me about a decision you modelled where the evidence was weak. How did you frame it and what happened?

    A strong answer names the options, constraints and assumptions, and says what the model recommended and what followed. They should admit where it was wrong. Worry if they describe only the analysis, or claim everything went to plan.

  2. How do you decide whether more data or another experiment is worth collecting before a choice is made?

    Look for value of information thinking: what could change the decision, how likely that is, and what it costs to wait. Good candidates mention time and cost. Worry if the answer is simply that more data is always better.

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

Job description you can post

We are hiring a Decision Scientist to help us make better choices under uncertainty. You will frame the decisions that matter, model the options, design the evidence we need and measure whether our calls worked. You will work closely with product, engineering and research.

What you will do

  • Frame high-stakes product and research decisions with clear options and constraints
  • Build decision models that state assumptions and uncertainty openly
  • Estimate the value of extra information before we commit to collecting it
  • Design experiments and causal analyses to test key assumptions
  • Write outcome measurement plans and review results against predictions
  • Present recommendations to decision owners in plain language

What you will bring

  • Demonstrated work turning uncertain evidence into a recommended decision
  • Strong grounding in probability, statistics and experimental design
  • Practical experience with causal inference where randomisation is limited
  • Working knowledge of decision theory and value of information
  • Fluent in Python or R for analysis and modelling
  • Clear written and spoken communication with non-specialists

Nice to have

  • Experience with experiment platforms
  • Background in econometrics or applied economics
  • Experience evaluating AI-driven products

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

Skills that matter

  • Causal inferenceAdvancedSeparate causal claims from predictive associations
  • Experimental designAdvancedChoose comparisons that identify credible effects
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Decision theory and game theoryWorkingAnalyse choices, incentives and strategic interactions

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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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