Data Scientist

About the role

Hewlett Packard Enterprise is hiring a Data Scientist in a hybrid arrangement, working on average two days per week from an HPE office. The role embeds analytics and data-driven decision making into business processes and operational workflows, combining business acumen, statistical methods, machine learning techniques and data visualization to solve complex business problems.

Key responsibilities

• Analyze, validate and interpret data sets, analytics solutions and user requirements
• Collect, clean, transform and validate data from multiple sources for reporting, analytics and machine learning
• Build interactive dashboards, reports and visualizations with Power BI and other analytics tools
• Perform exploratory data analysis, statistical analysis, segmentation, trend analysis and hypothesis testing
• Support the development, testing and deployment of predictive and machine learning models for forecasting, classification, customer behavior analysis and operational optimization
• Collaborate with cross-functional stakeholders to turn business requirements into data-driven solutions
• Take part in data engineering and automation, including data preparation, ETL and repeatable analytics workflows
• Communicate findings and recommendations to technical and business audiences

Required profile

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Operations Research, Engineering, Analytics or a related quantitative discipline
• Experience with analytics, reporting, business intelligence, statistical analysis, data visualization or machine learning projects through academic, internship or professional work
• Strong analytical thinking, problem-solving and critical reasoning skills
• Ability to explain technical concepts to business stakeholders and strong written and verbal communication
• Knowledge of customer support, operations, service management or business performance metrics is a plus

Required skills

• Basic to intermediate SQL
• Power BI or similar business intelligence and data visualization platforms
• Statistical analysis: hypothesis testing, regression, forecasting, segmentation, trend analysis
• Familiarity with Python, R or equivalent analytics languages
• Machine learning concepts: classification, clustering, predictive modeling, model validation
• Data preparation, ETL, data quality and structured/unstructured data integration