requirements and translate them into technical solutions. Utilise Python libraries and frameworks such as NumPy, Pandas, SciPy, and scikit-learn to perform data manipulation, statisticalanalysis, and machine learning tasks. Optimise code for performance, scalability, and maintainability, ensuring adherence to coding standards and best practices. Requirements: Proven experience more »
protocol positions or established institutions. Working with the Strategic Engagement Coordinator, you will track trends in priority audiences through direct engagement, research, polling and statisticalanalysis and analytics; as well as design and recommend PD strategies to connect with priority audience sectors to promote understanding of U.S. policy. … States are all required. Must have general knowledge of project management, including defining project objectives, outcomes, and assessment methods. Knowledge of marketing techniques, market analysis and analytics, and customer service standards in the United States and United Kingdom; knowledge of trends in EOL and EV audience engagement; understanding of … and video skills are required. Must have good numerical skills to develop and manage project and grant budgets; must be able to develop descriptive statisticalanalysis of relevant audience segments and to describe the outcomes of Public Engagement activities and initiatives. Availability: Must be available to travel throughout more »
Job summary The Principal Data Scientists demand exceptional expertise and practical experience in machine learning, statisticalanalysis, and fraud detection. They must possess excellent communication and collaboration skills and be experts in detecting fraud using statistical methods and disseminating all products, including fraud detection models, into a … control and implementation to guidance, leadership, and direction of the team. oProven practical experience in fraud detection methods using data including the implementation of statistical modelling and methods into a live production environment oPractical experience in designing algorithms, using statistical and problem centric methods to design actionable outcome … across a variety of data types. oExtensive practical experience in data science including the implementation of statistical modelling in a real-world environment oExperience and strong proficiency in programming languages for data science, e.g., SQL, R and Python alongside the ability to use tools and packages such as Alteryx more »
in data engineering. oExperience/understanding of data lifecycle management frameworks and project management methodology. oExperience in technologies relating to Data Science, for example: statisticalanalysis, machine learning, or natural language processing. oExperience with relational SQL and databases. oExperience with Azure cloud services. oMaster's degree, in relevant more »
