Meter Data Analyst - Remote AI Research Project

What You'll Do10

• Review AI-generated answers about utility metering, customer operations, and energy billing for factual accuracy.
• Create expert training examples that show correct utility workflows and customer data management.
• Evaluate AI output for relevance to energy industry tasks, including meter data analysis and billing processes.
• Flag errors, missing context, or flawed reasoning in AI responses.
• Apply hands-on knowledge of Oracle MDM, Oracle CC&B, SAP, or Power BI to judge whether AI content matches real system behavior.
• Write clear explanations that support your ratings and corrections.
• Follow project guidelines for style, scope, and data handling.
• Complete assigned review tasks within active project timelines.
• Coordinate with AI research teams through written updates and asynchronous channels.
• Contribute domain insight to improve model understanding of utility customer data.

Requirements7

• At least 2 years of professional experience in utility metering, customer operations, or a related energy function.
• Background in roles such as Meter Data Analyst, Customer Ops Analyst, Billing Analyst, Energy Accountant, or equivalent energy industry position.
• Hands-on experience with one or more of these tools: Oracle MDM, Oracle CC&B, SAP, Power BI.
• Strong written communication skills for explaining judgments and corrections.
• Attention to detail when checking data, billing records, and AI output.
• Ability to work on your own schedule and manage tasks without direct supervision.
• No previous AI experience required.

Who Should Apply

Energy professionals with at least two years in utility metering, customer operations, or energy billing will fit this project best. The role suits someone who has used Oracle MDM, Oracle CC&B, SAP, or Power BI in a real job and can explain how those systems work. Candidates who want a permanent full-time position, need constant team interaction, or have no energy industry background will find less value here. Applications often score low when the candidate lists energy tools but cannot show hands-on use, or when they lack the required two years in a relevant function. A clear writing sample that walks through a meter data or billing problem also helps separate strong applicants.

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