At HPE, we bring together the brightest minds to create breakthrough technology solutions and advance the way people live and work. Our legacy inspires us as we forge ahead dedicated to helping our customers make their mark on the world.
Learning does not only happen through training. Relationships are among the most powerful ways for people to learn and grow, and this is part of our HPE culture. In addition to working alongside talented colleagues, you will have many opportunities to learn through coaching and stretch assignment opportunities. You’ll be guided by feedback and support to accelerate your learning and maximize your knowledge. We also have a “reverse mentoring” program which allows us to share our knowledge and strengths across our multi-generation workforce.
- Participates in the analysis and validation of data sets/solutions/user experience.
- Aids in the development, enhancement and maintenance of a client's metadata based on analytic objectives. May load data into the infrastructure and contributes to the creation of the hypothesis matrix. Prepares a portion of the data for the Exploratory Data Analysis (EDA) / hypotheses.
- Contributes to building models for the overall solution, validates results and performance. Contributes to the selection of the model that supports the overall solution.
- Supports the research, identification and delivery of data science solutions to problems.
- Supports visualization of the model's insights, user experience and configuration tools for the analytics model.
- Embeds analytics into client’s business processes and applications. Combines business acumen and scientific methods to solve business problems.
Education and Experience Required:
- Bachelor´s degree in Statistics, Operations Research, Computer Science or equivalent.
Knowledge and Skills:
- Basic knowledge of data science methodologies.
- Basic understanding of business requirements and data science objectives.
- Basic data mapping, data transfer and data migration skills. Basic understanding of analytics software (eg. R, SAS, SPSS, Python).
- Basic knowledge of machine learning, data integration, and modeling skills and ETL tools (eg. Informatica, Ab Initio, Talend).
- Basic communication and presentation skills.
- Basic data knowledge of relevant data programming languages.
- Basic knowledge of data visualization techniques