Tags: EagleProfessionalResources jd DataAnalyst 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.