BASIC QUALIFICATIONS

• Bachelor’s Degree in Statistics, Mathematics, Computer Science, Information Systems, or related field.
• 5-8 years of experience in analytics, automation, and reporting.
• Proficiency with SQL, Python, or R.
• Experience with Power BI, Tableau, or similar visualization tools.
• Familiarity with cloud data platforms and big data technologies.
• Background in advanced analytics, data science, machine learning, or GenAI is a plus.
  Our Lead Advanced Analytics earn between $130,700 - $196,100 USD Annual. Not to mention all the other amazing rewards that working at AT&T offers. From health insurance to tuition reimbursement and paid time off to discounts on products and services just to name a few. There is a lot to be excited about around here. Individual starting salary within this range may depend on geography, experience, expertise, and education/training.

ABOUT THE ROLE

In this role, you will apply strong business acumen and technical skills to analyze complex and varied data, answering critical business questions and providing actionable insights. Your deep understanding of the business will guide how you approach problem-solving and data interpretation. Building on this foundation, you will design and implement automation in reporting and analytics, as well as establish robust data quality audits to ensure the accuracy and reliability of insights.
Your work will enable AT&T’s converged and wireless product teams to make informed decisions with confidence, supported by efficient, scalable, and trustworthy analytics solutions. You’ll be part of a collaborative and innovative team that values growth, encourages new ideas, and supports your success. Together, we’re transforming how AT&T delivers value through smarter, automated analytics.

WHAT YOU’LL DO

• Leverage business knowledge and technical expertise (SQL, Python, R) to analyze complex, disparate data sources.
• Develop and maintain automated, self-service reporting tools for converged and wireless product KPIs.
• Support wireless product reporting and pricing strategies through insightful analysis.
• Create clear and effective visualizations and dashboards for diverse audiences.
• Implement data quality auditing and alerting to identify and resolve data anomalies.
• Continuously improve automation workflows to enhance efficiency and reduce manual effort.
• Collaborate with cross-functional teams to evolve analytics capabilities aligned with business goals.