From Ideation to Production in a Start-up

From Ideation to Production in a Start-Up

Working at a starting business is different. It is exciting and challenging, energizing and character building. At Energetech, talent from all corners of the Power and Gas markets came together to build a strong foundation to tackle the challenges of the energy transition.    

I am coming from an established company with specific resources dedicated to each part of the enterprise, from IT infrastructure, over a data acquisition team, a team of data scientists who develop models to a trader executing strategies. There are clear borders of responsibilities, which provides more time to focus but fewer possibilities to grow in multiple areas. Here at Energetech, my role is very fluid, and allows me the possibility to use my computer science background in the development of the IT infrastructure, use my knowledge of the markets to develop models, and to build tools for reporting and real time observation of market activities.

The smaller nature of the business incentivizes everyone to participate and engage in the successful creation of new models.  

Our team is comprised of people with different backgrounds and versatile knowledge, which provides for fruitful, open discussions. Ideas are created, dissected, and evaluated for feasibility; guided by more senior colleagues and challenged by younger team members.

As a systematic and fact-driven company, we are basing our decisions on a strong foundation of data. Oftentimes, the data is not immediately at hand, and sources must be investigated, vendors compared, free data scraped and made available to everyone in a centralized manner. Data needs to be cleaned, validated, and enriched. In the process, we gain more insights to the data than we might gain by just using someone else’s data.

When the data is gathered, the data scientists dig deeper into the matter, investigating the data for correlations, trends, or other factors that might explain events in the market. The data scientist toolbox is very vast, so we can make use of statistical approaches, simulations, or black box machine learning models. The flat hierarchy is a very important aspect of Energetech, everyone is heard out and we see eye-to-eye with each other.  

Philipp Freiberger

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