Data Scientist

Data Scientist

  

This role has been designated as ‘Remote/Teleworker’, which means you will primarily work from home.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know diverse backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description:

   

Job Family Definition:

Designs, develops and applies programs, methodologies and systems based on advanced analytic models (e.g. advanced statistics, operations research, computer science, process) to transform structured and unstructured data into meaningful and actionable information insights that drive decision making.

Uses visualization techniques to translate analytic insights into understandable business stories (eg. descriptive, inferential and predictive insights).

Embeds analytics into client’s business processes and applications. Combines business acumen and scientific methods to solve business problems.

Management Level Definition:

Contributions have visible technical impact on a product or major subcomponent.  Applies in-depth professional knowledge and innovative ideas to solve complex problems. Visible contributions improve time-to-market, achieve cost reductions, or satisfy current and future unmet customer needs. Recognized internal authority on key technology area applying innovative principles and ideas. Provides technical leadership for significant project/program work. Leads or participates in cross-functional initiatives and contributes to mentorship and knowledge sharing across the organization.

Responsibilities:

• Defines and develops the value proposition to lead the formulation and definition of analytics solution objectives and technical requirements based on user needs, an understanding of business value industry requirements and advanced analytic models (statistical, operations research, computing, process).
• Conceptualizes, builds, develops, and enhances a client's analytic model.  Determines the right modeling methodology to the use case, available structured and unstructured data, cost and timing constraints to solve the large and complex business issues and delivers compelling and clear business focused insights.
• Embeds analytic models into enhanced large scale business processes and operational systems by collaborating with Application Developers.
• As a recognized authority, applies analytic methods and adds to problem domains.
• Using unique visualization techniques, condenses large volumes of complex ideas into elegant and simple visual models.
• Take ownership of developing and maintaining the logic for building the Eligible Base for the IB business, including implementing future enhancements to ensure accuracy, scalability, and alignment with business needs.
• Use advanced analytical techniques to uncover trends, generate actionable insights, and identify new opportunities to drive growth and performance in the OS business.
• Influences a client's strategic decisions by using deep industry expertise and deploying innovative analytics solutions in the operational systems

Education and Experience Required:

• PhD degree in Statistics, Operations Research, Computer Science or equivalent and 5+ years of relevant experience. Or Master´s Degree in these areas and at least 5-6 years of relevant experience.

Knowledge and Skills:

• Extensive knowledge of data science methodologies including but not limited to classical regression, neural nets, CHAID, CART, association rules, sequence analysis, cluster analysis, and text mining.
• Ability to translate business requirements into mathematical models and data science objectives to achieve measurable business outcomes.
• Extensive understanding of analytics software (eg. R, SAS, SPSS, Python).  Advanced understanding of analytics deployment architectures.
• Extensive machine learning, data integration and mathematical modeling skills and ETL tools (Informatica, AbInitio, Talend).
• Advanced communication and presentation skills.
• Excellent interpersonal skills and effectiveness in working across geographical boundaries.
• In-depth knowledge of programming languages such as Python, SQL, R, SAS, Java, Unix Shell scripting.