Data Scientist
Hewlett Packard Enterprise (HPE)Match Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, mid-level Data Scientist title, metro location, and broad ML requirements increase competition.
Specialized ML/measurement and conversational-AI experience is somewhat transferable but expects domain-specific support-analytics expertise.
Requires master's/PhD, 5-7 years, specific ML/statistics and platform experience, making filters strict.
Job Description
Structured overview of role & requirementsAbout This Role
Develop and implement advanced data collection, analysis, and machine learning solutions to optimize digital customer experiences and business outcomes in networking support services.
Collaborate with cross-functional teams including data engineering and business units to build scalable data models, analytics solutions, and dashboards using platforms like Power BI and Databricks.
Lead efforts in discovering user behavior patterns, process improvements, and support training and mentoring of junior data scientists within the Digital Experience & Automation team.
Minimum Requirements
Master’s or PhD degree in business administration, economics, computer science, management information systems, or related field or equivalent experience.
5-7+ years of experience in Data Science, Machine Learning, or Applied Research focused on measurement frameworks or systems.
Expertise with statistical analysis, machine learning algorithms (clustering, regression, dimensionality reduction), and advanced analytics tools including Python, Snowflake, Databricks, and Power BI.
Hybrid work model requiring approximately 2 days per week onsite at HPE office (location: Not explicitly mentioned in the JD).
Ideal Candidate Profile
Experienced in applying machine learning to real-world business problems involving customer journey analytics, conversational AI data, and omnichannel support metrics.
Skilled at collaborating across multiple teams including technology providers, engineering, and business stakeholders to drive data-driven decision making and automation.
Proficient in designing and operationalizing measurement frameworks, experiments, and advanced visualization techniques to communicate insights effectively to both technical and non-technical audiences.