Modelling Advancement Lead

Department for Transport
Leeds
Modelling Advancement Lead

Department for Transport

Apply before 11:55 pm on Sunday 5th April 2026

Location: Birmingham, Leeds, London (Hybrid)

Salary: £57,515 - £62,034 - A Civil Service Pension with an employer contribution of 28.97%
National: £57,515

London: £62,034

️ Contract Type: Permanent– Flexible working, Full-time, Job share, Part-time

️ Available Positions: 1

We currently have two modelling teams:

* Modelling Insights (MI): This team leads the development of national transport models that estimate travel demand and road traffic. They ensure TASM’s modelling and analyses are operated and managed under efficient frameworks and digital infrastructure. They also modernise our modelling capabilities and work closely with data engineers, social researchers, and policy teams to provide high‑quality insight.

* Cross‑Modal Modelling (CMM): This newer team focuses on modelling the whole transport system by integrating transport modes and understanding how they interact across portfolios. They build connections and collaborate with other analytical disciplines to ensure the decision-making processes are supported with stronger evidence from a multimodal angle. Their work includes land‑use modelling, freight modelling, activity‑based modelling, and developing multi‑modal guidance for TAG.

Across both teams, we have 11 analysts with a range of skillsets who collaborate closely, share ideas openly, and enjoy learning and developing together. We value diverse perspectives, innovative thinking, and a supportive working environment. Our work plays a key role in delivering DfT’s strategic priorities

Top Responsibilities

* Lead transformation to our modelling capabilities with Google Cloud infrastructure

* Integrate our modelling workstreams and redesign data pipelines

* Visualise and communicate modelling insights clearly

* Embed scenario‑based analysis into our modelling approach

* Explore innovative techniques (e.g. machine learning) to build smarter, faster models that improve our capabilities

* Deliver research to improve modelling data and accelerate model developments

* Manage one Senior Transport Modeller

Benefits

* Employer pension contribution of 28.97% of your salary. Read more about Civil Service Pensions here

* 25 days annual leave, increasing by 1 day each year of service (up to a maximum of 30 days annual leave).

* 8 Bank Holidays plus an additional Privilege Day to mark the King’s birthday.

* Access to the staff discount portal.

* Excellent career development opportunities and the potential to undertake professional qualifications relevant to your role paid for by the department, such as CIPD, Prince2, apprenticeships, etc.

* Joining a diverse and inclusive workforce with a range of staff communities to support all our colleagues.

* 24-hour Employee Assistance Programme providing free confidential help and advice for staff.

* Flexible working options where we encourage a great work-life balance.

About You

We’re looking for people who bring passion, curiosity, and a willingness to learn. If you enjoy solving problems, thinking creatively, and working with colleagues from different backgrounds to deliver meaningful insights, you’ll fit right in. With your strong technical capabilities, you will drive change, improve how we model and analyse transport, clearly communicate impacts to a range of audiences, and confidently bring your own perspective to discussions.

A strong interest in programming and automation will really help you succeed, and we’re keen to support you in developing new skills that strengthen TASM’s analytical capability.

We know great analysis comes from teams with a wide mix of experiences. We welcome applications from people with strong skills in one or more of the following areas:

* Transport Modelling

* Operational Research

* Data Engineering

* Data Science

How to Apply

Read the full description and apply at CS Jobs using the link provided.

This vacancy closes at 23:55 on Sunday 5th April 2026
Posted 2026-03-24

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