Develop statistical, mathematical, econometric, causal-inference, simulation, and machine-learning models using large-scale aviation datasets.
Analyse airport surface, runway, terminal airspace, and multi-airport system operations, including traffic-flow and delay-propagation mechanisms.
Develop capacity-estimation, demand-forecasting, performance-benchmarking, and what-if assessment methods to support planning and operational decision-making.
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Develop capacity-estimation, demand-forecasting, performance-benchmarking, and what-if assessment methods to support planning and operational decision-making.
Investigate the operational, environmental, safety, and economic impacts of airport capacity interventions and sustainable aviation measures.
Process, integrate, visualise, and validate flight trajectory, operational, weather, and other relevant transportation datasets.
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Able to work well both independently and as a member of a team
DOS believes in the career development and training of our staff to bring out their best potential at work
You will be equipped with skills and subject matter know-how via in-house and external learning and development opportunities. You will also be exposed to different statistical work via our structured job rotation programme.
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Managing day-to-day media requests, drafting media replies, and monitoring and disseminating daily media coverage.
Conceptualising, planning, and managing content for DOS's social media channels and developing digital and integrated marketing campaigns to grow DOS's social media presence and reach.
Analysing the effectiveness of communications channels and tools, and proposing effective solutions for profiling DOS’s data and data services to various stakeholders, including students, businesses, the media and public sector officers.
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Exploring new technologies and analytics solutions, and using depths of knowledge in statistics and various machine learning tools to forecast and classify patterns in the data.
Increasing performance and accuracy of machine learning algorithms through fine-tuning and further performance optimisation.
Liaising and working closely with team and clients to clearly define and establish the requirements for each task.
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Build and operate the harder engineering layers of a solution when projects demand it: orchestrated pipelines, worker-based and event-driven architectures, stream processing, and scaling for data volume and concurrency.
Own solution quality: data validation, model evaluation, reproducibility, performance, and operational reliability (monitoring, alerting, failure recovery).
Lead and mentor a team of junior data scientists and analysts; review their work, unblock them technically, and grow their skills.
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