AI-driven energy trading and forecasting platform for North American power markets
CWP Energy trades power and natural gas across North America using proprietary algorithms and machine learning models. The tech stack—Angular, React, C#/.NET, Python, Kafka alternatives (Airflow, Dagster), Kubernetes, Docker—reflects an engineering organization building for real-time, data-intensive operations. Active projects center on modernizing their forecasting engine and consolidating fragmented tools (scheduling, optimization, trading analytics) into a unified SaaS product, addressing an internal pain point around handling complex data at scale.
CWP Energy operates as a proprietary trading and energy services firm, executing trades on North American ISO power and natural gas markets. The company combines human traders and analysts with machine learning models to identify arbitrage and optimization opportunities. Their product roadmap centers on a next-generation SaaS platform that unifies forecasting, asset optimization, and trading analytics—replacing what appears to be a patchwork of legacy systems. The org is compact (11–50 employees, based in Montreal) with a senior-heavy hiring profile, suggesting focused builds on core capabilities rather than team scaling.
Core stack: Angular, React, C#/.NET, Python, Kubernetes, Docker, SQL. Data pipelines use Apache Airflow and Dagster. No adopting or replacing signals visible in recent hiring or project activity.
Montreal, Quebec, Canada. All current hiring is Canada-based. Founded in 2012.
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