Data Scientist II

Job Description

Data Scientist II | Hewlett Packard Enterprise

The Tone:
This is a hybrid role at Hewlett Packard Enterprise, requiring an average of 2 days per week from an HPE office. Hewlett Packard Enterprise is a global edge-to-cloud company that helps companies connect, protect, analyze, and act on their data and applications wherever they reside, from edge to cloud. The purpose of this role is to transform insights into outcomes at the speed required for success in today’s complex world, helping customers overcome IT complexity and deploy the right technology to respond quickly to market opportunities.

The TL;DR
• Role: Full Time
• Type: Hybrid
• Location: Hybrid

• Team: HPE Operations
• Mission: Transform structured and unstructured data into meaningful and actionable information insights that drive decision making.
• Tech Stack: R, Python, SAS, SQL, Docker, GitHub, Coding IDE platforms, Tableau, Alteryx

What You’ll Actually Do
• Define Objectives: Apply business knowledge to formulate and define analytic objectives, using available data elements and defining business rules.
• Prepare Data: Develop and maintain metadata, load data into infrastructure, create hypothesis matrix, and prepare for Exploratory Data Analysis.
• Build and Validate Models: Construct models to support solutions, validate initial models, and monitor results and performance post-implementation.
• Deliver Solutions: Research, identify, and assist in delivering data science solutions, contributing to the measurement of business performance based on deployed models.
• Visualize Insights: Create clear visualizations of model insights for easy consumption by stakeholders.

The Must-Haves
• Background: Mid-level professional with a Bachelor’s Degree in Statistics, Operations Research, or Computer Science, or a Master’s Degree in these fields.
• Experience: At least 2-3 years of relevant experience if holding a Bachelor’s degree. Working knowledge of data science methodologies including classical regression, neural nets, CHAID, CART, association rules, sequence analysis, cluster analysis, and text mining. Understanding of supply chain business requirements and their translation into mathematical models.
• Skills: Strong background and working proficiency in analytics software (R, Python, SAS, SQL) for model building, testing & deployment. Experience with Docker, GitHub, and Coding IDE platforms. Solid communication, presentation, and interpersonal skills, including working across geographical boundaries. Working knowledge of data visualization tools such as Tableau or Python.
• Bonus: Knowledge of Alteryx.