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Build an exciting, rewarding career with us – help us make a difference for millions of people every day. Consider joining the Duke Energy team, where you'll find a friendly work environment, opportunities for growth and development, recognition for your work, and competitive pay and benefits.

Position Summary

This role represents the career/senior level within the Operational Visualization job hierarchy. Professionals at this level operate with a high degree of autonomy, managing specialized work with minimal supervision. They are expected to demonstrate advanced skills, apply deep subject matter expertise, and independently handle complex or unique challenges. Fully proficient in relevant concepts and tools, they proactively identify problems, develop innovative solutions, and drive resolution.
This position is ideal for a data professional with strong analytical and statistical capabilities. The primary responsibility is to extract actionable insights from large and complex datasets to support business transformation, enhance employee engagement, and improve customer experience.

Responsibilities

This role plays a pivotal part in identifying operational gaps, uncovering opportunities for optimization, and driving improvements in billing, accounts receivable, and Voice of the Customer (VOC) processes through actionable, data-driven insights.

• End-to-End Analytics Ownership: Lead the full analytics lifecycle—from data extraction and transformation to advanced analysis, visualization, and presentation of insights.

• Advanced Analytical Techniques: Leverage statistical analysis, data mining, machine learning, and deep learning to uncover actionable insights and support data-driven decision-making.

• Data Science Application: Apply modern data science methodologies to solve complex business problems and inform strategic initiatives.

• Stakeholder Collaboration: Partner with cross-functional teams to understand business challenges, define analytical needs, and deliver tailored, high-impact solutions.

• Thought Leadership: Stay current with emerging technologies and best practices in analytics and data science, serving as a thought leader within the team and across the organization.

• Project Leadership: Lead analytics workstreams across one or more concurrent projects, ensuring timely, high-quality delivery aligned with business priorities.

• Data Extraction & Transformation: Extract and prepare data from SAP HANA, SQL Server, Redshift, and other sources using SQL and other relevant tools.

• Insight Generation: Analyze data to identify trends, detect anomalies, and uncover opportunities for improvement in Accounts Receivable (AR), Billing, and Revenue Services.

• Visualization & Reporting: Design and develop compelling Power BI dashboards and advanced analytics reports that clearly communicate insights and support strategic recommendations.

• Process Optimization: Provide data-backed recommendations to enhance operational efficiency, reduce revenue leakage, and improve overall process performance.

• Independent Problem Solving: Operate effectively in ambiguous situations, applying strong analytical and statistical techniques to drive measurable results.

• Cross-Functional Support: Contribute to broader initiatives, including Revenue Services transformation and Voice of the Customer (VOC) analytics, to support enterprise-wide goals.

Required/Basic Qualifications

• Bachelor's degree

• In addition to required degree, five (5) years minimum of related work experience

Desired Qualifications

• Proficiency in Power BI, SQL, SAP HANA, with a strong foundation in building dashboards and visualizing complex data.

• Experience with Python for data analysis, automation, and machine learning applications.

• Familiarity with data orchestration tools, Dagster, and version control systems, Git, GitHub.

• Working knowledge of Power Platform tools, Power Automate, Power Apps.

• Exposure to Natural Language Processing (NLP) techniques and their application in business analytics.

• Experience working with CI/CD pipelines to automate and streamline analytics workflows, including deployment of dashboards, models, and data pipelines.

• Ability to work with large, complex datasets to perform statistical analysis and generate actionable insights.