Data Scientist

Hewlett Packard Enterprise•Spring, TX

2d•Onsite

About The Position

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. Contributes to assignments of limited scope by applying technical concepts and theoretical knowledge acquired through specialized training, education, or previous experience. Acts as team member by providing information, analysis and recommendations in support of team efforts. Exercises independent judgment within defined parameters.

Requirements

Bachelor´s degree in Statistics, Operations Research, Computer Science or equivalent.
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.

Responsibilities

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.