Alan Roberts
Modelling Team Lead @Umas
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WORK HISTORY
Modelling Team Lead @Umas
London, GB
Key responsibilities:• Definition of processes and protocols for good practice and governance across data and analytics.• Capability planning for company models based on market and company strategic aims.• Oversight of analytics work and responsible for technical QA processes.• Management of analytics code repositories, AWS buckets and databases.• Responsible for maintenance and development of key AIS emissions model (FUSE).• Data and analytics lead for project proposal development and reviews.• Management of client relationships and hands-on fulfilment of projects with complex data and analytics requirements.• Facilitation of technical knowledge-sharing and dissemination across the company.Key achievements:• Rearchitecture of emissions modelling code into Spark framework. Streamlined more robust pipeline resulting in massive cost and run-time savings [circa 20x] Facilitation of agile development cycle• Spearheaded development of high fidelity activity-based voyages-ports ecosystem• Development of team-based collaborative analytics culture• Development and implementation of model confidence metrics• Creation of common spatial analytics python librarySelected projects:• Appraisal of behavioural analytics (nudging) study on behalf of client.• Development of novel algorithms for voyage-identification from AIS data.• Port congestion metric design, inference and analysis for the global fleet.• Report on Arctic shipping emissions for a client, including processing and mapping of large proprietary AIS dataset.• Market appraisal for vessel electrification.
EDUCATION
University of Cambridge
PhD, Geophysics
University of Cambridge
BA (Hons), MSci, Natural Sciences (Experimental and Theoretical Physics)
SKILLS
ABOUT ALAN ROBERTS
Experienced Data Scientist and technical lead specialising in industrial and spatial analytics, optimisation, and large-scale distributed computing.I bring more than 20 years’ experience across data analytics, simulation, and scientific modelling, with a track record of designing and operationalising robust systems in transport, infrastructure, emissions and climate-related domains.My work focuses on building scalable, high-integrity analytics platforms using Python, Spark and AWS, combining strong statistical foundations with engineering discipline. A highlight has been leading the re-architecture of large spatio-temporal modelling systems into distributed Spark pipelines, delivering substantial improvements in performance, cost-efficiency, reproducibility and long-term maintainability. Others have included overseeing the design, development and deployment of novel activity identification algorithms, and the simulation of a trillion row smart city traffic dataset, as well as building an emissions inventory for a major world port.Alongside hands-on technical work, I seek to establish best practice in data quality, validation, governance and uncertainty quantification, ensuring models are explainable, auditable and reliable in real-world operational contexts. I enjoy mentoring data scientists and contributing to collaborative, low-ego engineering cultures focused on sustainable delivery.I am particularly motivated by challenges at the intersection of optimisation, engineering and sustainability, where rigorous modelling and trusted data systems can support decarbonisation, resilient infrastructure and better long-term decision-making.
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