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Tags: [[tag:EagleProfessionalResources]] [[tag:jd]] [[tag:DataAnalyst]] [[tag:DataScientist]] This is a composite of job descriptions related to Data Analyst / Scientist Skills and Qualifications The qualified candidate must have: - [X] A Master’s in Information Technology, Computer Science, or a related quantitative discipline; - [ ] Five plus (5+) years’ experience in data science and in senior engineering and technology roles working with product development teams, delivering and building digital products; - [ ] Simulation modeling experience using AnyLogic; - [ ] Understanding of high performance algorithms and Python statistical software; - [ ] Experience with lamda architectures and batch and real-time data streams; - [ ] Experience in industry data science (i.e. machine learning, predictive maintenance) preferred; - [ ] Experience architecting highly scalable distributed systems, using different open source tools; - [ ] Experience with agile or other rapid development methods; - [ ] Experience in object-oriented design, coding and testing patterns as well as experience in engineering software platforms and large-scale data; - [ ] Deep knowledge of data modeling and understanding of different data structures; and, - [ ] The successful candidate will be responsible for: - [ ] Bringing deep functional expertise to shape data structures and algorithms in a distinctive way to ensure large-scale business impact of the digital products being built and drive competitive advantage; - [ ] Collaborating with Data Head and Developers to find opportunities to use company data to drive business solutions; - [ ] Mining and analyzing data from company systems; - [ ] Assessing the effectiveness and accuracy of new data sources and data gathering techniques; - [ ] Developing custom data models and algorithms to apply to data sets; - [ ] Using Machine Learning and Artificial Intelligence to increase and optimize customer experiences, revenue generation, and other business outcomes; - [ ] Partnering with different functional teams to implement models and monitor outcomes; - [ ] Conducting data wrangling munging, exploration, sampling, training data generation, feature engineering, model building, and performance evaluation); - [ ] Enabling big data and batch/real-time analytical solutions that use emerging technologies; - [ ] Coding, testing, and documenting new or modified data systems to create robust and scalable applications for data analytics; and, - [ ] Ensuring all automated processes preserve data by leading the alignment of data availability and integration processes.
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