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optimization / analytics / energy / delivery

Energy Analytics & Enterprise Delivery

Sellable analytics packages and enterprise delivery across accounts totaling 12 GW+ of installed capacity.

problem

Energy operators need analytics that reflect their asset types, reporting obligations, and actual operational decisions—not generic model outputs.

approach

Built reusable vertical packages, translated client needs into product specifications, and shipped analytics code directly into the Django platform.

impact

Packages became the standard offering for new customers; I owned delivery from engineering sessions to executive reviews for 12 GW+ of accounts.

Field notes / Energy systems
Physical systems. Evidence-backed decisions.Oil & Gas · Wind · Solar · Battery storage

Domain expertise made reusable

As Data Scientist II, I built Narrativewave’s sellable analytics packages per vertical. They became the standard offering for new customers, connecting technical implementation with a repeatable commercial and delivery model.

Solar and Wind

Performance Engineering and Real-Time packages covered dozens of event types, IEC-standard downtime and availability classification, and NERC GADS reporting. Solar work also accounted for asset hierarchy and interconnection-aware logic.

Battery energy storage

The BESS package addressed market participation and resource optimization. Its operational context differs from generation assets, so the package reflects storage-specific use cases rather than reusing a generic dashboard.

Enterprise ownership

I owned delivery for accounts totaling 12 GW+ of installed capacity across leading US renewable IPPs and Oil & Gas operators. That included weekly sessions with site managers and performance engineers, executive reviews, and QBRs.

The 12 GW+ figure describes the accounts under my delivery responsibility; it is not a claim that I individually built or controlled those assets.

Between the customer and Product

I led discovery sessions, translated customer needs into feature specifications, co-created features with Product, and shipped analytics code into the Django platform through product tickets.

I also supported pre-sales by scoping use cases, demonstrating packages, and validating feasibility, while mentoring the Data Scientist I team on implementation and delivery standards.

The asset illustrations represent industry domains, not specific client installations or endorsements.

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