Microsoft Senior Data Analyst 

United States, Washington 

672229442

Required/Minimum Qualifications

• Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • OR Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 2+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • OR equivalent experience.

Additional or Preferred Qualifications

• Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 6+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • OR Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • OR equivalent experience.
• Demonstrated analytical and problem-solving skills.
• Proficiency in data analysis tools and software.
• Experience with statistical and machine learning models.
• Demonstrated understanding of data visualization techniques, BI systems
• Experience with Mobile app, Media, Social, influencer partnership

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Identify and promote methods that enhance efficiency in analytics and reporting.
• Ensure solutions are reusable, easily discoverable, and self-service oriented.
• Drive meaningful data interpretation to inform business decisions.
• Recommend and socialize optimal methods for operationalizing, sharing, and scaling insights.
• Share knowledge and practical rationale for transitioning ad-hoc analyses to regular reporting features.
• Share domain knowledge to create clarity and ensure readiness to leverage data and insights effectively.
• Evaluate the viability of automated methods for data collection, reporting, and analysis.

• Understand the relationship between analytical models and business objectives.
• Establish clear linkage between generated models and desired business outcomes.
• Assess the degree to which models meet business objectives and address gaps.
• Ensure alignment on definitions and standards across stakeholders.
• Define, design, and promote appropriate feedback and evaluation methods.
• Coach and mentor less experienced engineers as needed.
• Present results and findings to senior stakeholders.

• Leverage working relationships within and across teams to ensure alignment and quality execution.
• Drive the adoption of recommended data sources and analysis practices.
• Proactively engage stakeholders to identify and act on opportunities to leverage data and resources.
• Consult across teams on decisions related to data sourcing, analyses, and interpretation of results.

• Share insights and analytical experience through various means, including dashboards, reports, and visualizations.
• Synthesize and simplify details across analyses to highlight relevant findings.
• Identify opportunities to improve the efficiency of insights reporting techniques.
• Guide others and establish partnerships with stakeholders to ensure results are accessible and relevant.

Other

• Embody our and