3 of 3 Data Science Jobs in Maidenhead

growing world. We leverage cutting-edge technology to optimize operations, enhance customer experience, and drive business growth. We are seeking a Senior Data Engineer to join our team and play a pivotal role. About The Role At Maersk, we are redefining global logistics through data, platform engineering … building scalable platforms and intelligent systems that enable faster decision-making, operational efficiency, and seamless integration across the enterprise. As a Senior Data Engineer, Data & AI, you will design, build, and operate scalable data products, pipelines, and analytical foundations that power business insights, critical business capabilities ...

scientific expertise that supports regulatory, payer, and health technology assessment (HTA) decision-making.In this role, you will apply advanced biostatistical methodologies to real-world data sources including claims databases, electronic health records (EHR), registries, and linked data assets. You will contribute to the design, execution, and interpretation … thousands of colleagues worldwideYour responsibilitiesLead the design, analysis, and interpretation of non-interventional and RWE studies utilizing claims, EHR, registry, and other real-world data sourcesApply advanced causal inference and confounding-control methodologies including propensity score matching and weighting, inverse probability weighting, instrumental variable methods, marginal structural models ...

expertise that supports regulatory, payer, and health technology assessment (HTA) decision-making. In this role, you will apply advanced biostatistical methodologies to real-world data sources including claims databases, electronic health records (EHR), registries, and linked data assets. You will contribute to the design, execution, and interpretation … colleagues worldwide Your responsibilities Lead the design, analysis, and interpretation of non-interventional and RWE studies utilizing claims, EHR, registry, and other real-world data sources Apply advanced causal inference and confounding-control methodologies including propensity score matching and weighting, inverse probability weighting, instrumental variable methods, marginal structural models ...