Mobility Data Consultant

This is a hands-on, impact-driven role where you will apply your expertise in machine learning, statistical modelling, and data engineering to real-world transport datasets. You will also explore emerging technologies such as natural language processing (NLP), computer vision, and digital twins to unlock new insights and capabilities in transport planning. 

 

In addition to technical delivery, you will play a key role in client engagement, solution design, and internal capability building. You will contribute to the development of reusable tools, scalable workflows, and knowledge assets that elevate the team’s digital maturity. You will also have opportunities to mentor junior analysts, contribute to thought leadership, and collaborate on international projects. 

 

This role is ideal for someone who is curious, collaborative, and committed to using data and technology to create meaningful change in how people and goods move through our cities. 

Responsibilities 

Design and implement AI-powered transport solutions using machine learning, statistical modelling, and geospatial analytics to support smarter planning, operations, and policy decisions. 

Develop predictive models for transport demand, route optimisation, and travel behaviour using techniques such as time series forecasting, regression analysis, and classification algorithms. 

Apply advanced analytics including natural language processing (NLP) and computer vision to extract insights from unstructured data sources such as policy documents, user feedback, and traffic video feeds. 

Build and maintain scalable data pipelines and analytical workflows for processing large-scale transport datasets, including GPS traces, ticketing data, and sensor feeds. 

Create interactive dashboards and visualisations using tools like Power BI or Tableau to communicate complex insights to technical and non-technical stakeholders. 

Collaborate with transport planners, engineers, and digital teams to co-develop intelligent tools and platforms that enhance project delivery and client value. 

Support the development of digital twins and simulation models for urban mobility systems, enabling scenario testing and real-time decision support. 

Contribute to proposal writing, client presentations, and technical documentation, ensuring clarity, rigour, and alignment with project goals. 

Stay current with emerging technologies in AI, mobility analytics, and smart city infrastructure, and proactively identify opportunities to integrate them into ongoing work. 

Mentor junior analysts and data scientists, fostering a culture of learning, experimentation, and technical excellence within the team. 

 

Mandatory Skills  

Proven experience in applying supervised and unsupervised learning techniques such as regression, classification, clustering, PCA, Random Forest, and time series forecasting to transport or infrastructure datasets. 

Strong command of Python for data analysis, modelling, and automation. Familiarity with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, and OpenCV. 

Ability to build and manage scalable data pipelines using tools like SQL, PySpark, and Airflow. Experience with model lifecycle management using MLflow, Docker, or Kubernetes is a plus. 

Experience working with GIS data, GPS traces, GTFS feeds, and other transport-related datasets. Ability to integrate spatial data into analytical workflows and visualisations. 

Familiarity with NLP techniques and tools (e.g. spaCy, NLTK, Hugging Face) for extracting insights from policy documents, user feedback, and transport reports. 

Exposure to image and video analysis for traffic monitoring, infrastructure assessment, or safety analytics using OpenCV or deep learning frameworks. 

Ability to design clear, interactive dashboards using Power BI, or Plotly. Strong storytelling skills to communicate insights to technical and non-technical audiences. 

Understanding of transport planning principles, demand modelling, and urban mobility systems. Experience working with transport planners or infrastructure teams is highly desirable. 

Hands-on experience with cloud platforms such as Azure, AWS, or GCP, especially for deploying AI/ML models and managing data workflows. 

Experience working in cross-functional teams using agile methodologies. Ability to co-develop solutions with planners, engineers, and digital specialists. 

Excellent written and verbal communication skills in English. Ability to document technical work clearly and present findings to clients and internal stakeholders. 

Desired Skills 

Exposure to building or working with digital twins, agent-based models, or simulation platforms for transport networks, infrastructure, or urban systems. 

Familiarity with smart city applications, mobility platforms, and APIs (e.g. GTFS, OpenTripPlanner, Uber Movement) for integrated transport planning. 

Experience with GIS tools (e.g. ArcGIS, QGIS) and spatial analytics for mapping, accessibility analysis, and location-based insights. 

Hands-on experience deploying models on cloud platforms (Azure ML, AWS SageMaker, GCP Vertex AI), including use of serverless functions, containers, and CI/CD pipelines. 

Understanding of user interface and experience design principles to ensure data tools and dashboards are intuitive and impactful.

Ability to work with RESTful APIs, custom connectors, and automation frameworks to integrate external data sources and services. 

Interest in transport policy, behavioural economics, or urban sociology to contextualise data science outputs within real-world planning decisions.  

Experience working with international teams or clients, especially in transport or infrastructure domains, and ability to adapt communication styles accordingly. 

Passion for exploring emerging technologies and contributing to internal R&D, publications, or conferences. 

Required Qualifications 

Master’s degree in one of the following disciplines: 

Transportation Engineering 

Data Science / Artificial Intelligence 

Computer Science / Software Engineering 

Urban Analytics / Geospatial Studies 

Or a closely related technical field with strong quantitative and analytical focus 

Strong academic foundation in statistics, machine learning, and data-driven modelling, with demonstrated application in transport or infrastructure domains. 

Certifications in relevant technologies or platforms (e.g. Microsoft Azure ML, AWS Data Analytics, TensorFlow Developer, Power BI, or GIS tools) are a plus. 

 
Years of experience 

4 to 7 years of professional experience in data science, analytics, or AI roles 

Minimum 2 years of hands-on experience applying data science techniques to transport, mobility, infrastructure, or urban systems 

Experience working in cross-functional teams and delivering client-facing solutions in a consulting or project-based environment 

 

 

 

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