Hiring nowSupply chain, logistics and mobility analytics

Mobility Demand Scientist

Also called Transport AI Scientist

A Mobility Demand Scientist forecasts how and where people and goods will travel, then tests how transport capacity is allocated. They help a company put vehicles, routes and services where demand will be, and show the evidence.

What they do

They build models of travel demand from trip records, sensor feeds, schedules and external drivers such as events and weather. They turn those forecasts into inputs for allocation and routing decisions, often with an optimisation solver. They design tests that hold out whole time periods and whole areas, so the model is judged on places and moments it has not seen. They check calibration, look at where forecasts fail, and explain the gaps to operations teams. They also keep the data pipelines that feed the model in working order and document the assumptions behind each version.

What a strong hire delivers

In the first months, a strong hire delivers a working demand model on real data, with a clear baseline to beat. They set up independent temporal and spatial tests that the team trusts. They show how forecasts change allocation decisions and what that does to service coverage. They leave behind clean pipelines, written assumptions and a short list of known weak spots.

How you can tell they are good

Ask them to walk through a demand model they built and how they tested it. A strong candidate explains why they held out time and space separately, how they checked calibration, and what the model got wrong. They link forecast error to a real transport outcome. Be wary of anyone who quotes only a single accuracy score or cannot say how the forecast was used.

Why it is hard to fill

The title is new and rarely appears on a CV. The skills sit between transport modelling, forecasting and operations research, and few people have all three. Most candidates are strong in one and thin in another. Transport engineers may lack modern machine learning habits. Data scientists often lack feel for networks, capacity and constraints. Good people are usually settled in operational roles.

Where to find them

Look in transport planning consultancies, public transport operators and mobility platforms. Logistics and delivery businesses also employ people who forecast demand and plan capacity. Useful titles include transport modeller, operations research analyst, supply-chain analyst, demand planner and forecasting scientist. Academic transport research groups are another source, especially for people with strong spatial testing habits.

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 Mobility Demand Scientist

  1. Tell me about a demand forecast you built. What decision did it feed, and how did you know it was good enough?

    A strong answer names the decision, the baseline and the measure of success. They discuss calibration and error in terms of operations. Worry if they describe only model choice, or cannot say whether anyone acted on the forecast.

  2. How would you test a travel demand model so that it holds up in a new area or a new period?

    They separate temporal and spatial holdouts, avoid leakage between neighbouring zones and times, and check calibration as well as error. Worry if they rely on random splits, which flatter models on data with strong time and place structure.

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

Job description you can post

We are looking for a Mobility Demand Scientist to forecast travel demand and test how we allocate transport capacity. You will work with operations and engineering to turn forecasts into better planning decisions, and you will show clearly how well those forecasts hold up.

What you will do

  • Build and maintain models of travel demand across time and place
  • Design independent temporal and spatial tests for every model release
  • Check forecast calibration and report where it fails
  • Feed forecasts into allocation and routing decisions with constraints
  • Work with operations teams to measure service coverage and transport outcomes
  • Maintain data pipelines and document model assumptions

What you will bring

  • Demonstrated demand forecasting work used in real decisions
  • Strong grounding in probability, statistics and time-series modelling
  • Experience building reliable data pipelines
  • Hands-on use of optimisation or operations research methods
  • Clear habits in testing models on unseen periods and areas
  • Able to explain uncertainty to non-technical colleagues

Nice to have

  • Experience with routing engines or logistics systems
  • Background in transport engineering or transport planning
  • Experience with spatial data and network models

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

Skills that matter

  • Optimisation and operations researchAdvancedChoose actions subject to objectives and constraints
  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Data pipelinesWorkingBuild reliable ingestion and transformation systems
  • Probability and statisticsWorkingQuantify variability and statistical evidence

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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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