Job Description

Locations

• Austin, Texas, United States 78727

Salary

Skills Required

• data analyticsintermediate
• data storytellingintermediate
• analytics strategyintermediate
• business analyticsintermediate
• data explorationintermediate
• data analysisintermediate
• presenting resultsintermediate
• collaborationintermediate
• leadershipintermediate

Required Qualifications

• Minimum BS/BA in Computer Science, Software Engineering, Data Science, or equivalent degree (degree in computer science)
• 5+ years defining and leading business analytics initiatives, including surfacing insights, explaining outliers, building forecasting algorithms, and effectively communicating findings to stakeholders at all levels, including senior leadership (experience, 5 years)
• Demonstrable mastery of Python for data analysis, including proficiency in pandas, NumPy, scikit-learn, and data visualization libraries for stakeholder reporting (degree in data analysis)
• Self-directed problem-solver comfortable working through ambiguity, managing multiple priorities, and driving projects from definition through delivery (experience)

Preferred Qualifications

• MS/MA in Computer Science, Software Engineering, Data Science, or equivalent degree (degree in computer science)
• Experience collaborating with data engineering teams on pipeline design and data quality improvements (experience)
• Familiarity with cloud data platforms (AWS, Snowflake) (experience)

Responsibilities

• You’ll lead business analytics projects through all phases — defining investigations, exploring data, conducting analysis, and presenting results to business customers. Projects will span workforce planning, operational analytics, and strategic initiatives across Hardware Engineering.
• This role requires collaboration within a multi-disciplined, geographically distributed data science team, working closely with data engineering to build on our existing infrastructure and shape future analytics capabilities.
• Build repeatable analytical frameworks and dashboards that become the standard for how Hardware Engineering evaluates organizational health and plans for growth
• Inform strategic decisions across the HWE organization, from workforce planning to operational efficiency, through presentations, visual dashboards, and reports
• Build forecasting and optimization models for engineering resource needs, translating complex technical constraints into strategic recommendations for senior stakeholders
• Define analytics requirements in partnership with data engineering, owning the translation layer between infrastructure capabilities and business needs

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