Have you met: Adam Green

Written by Fabian Le Gay Brereton

Chief Executive Officer & Co-Founder BSc Computer Science (Hons) University of Western Australia

September 29, 2021

Adam Green, an energy engineer turned data scientist, joined Gridcognition as a Data Engineer in late 2020.

Having recently returned to New Zealand after an eight year stint in the UK and Germany, which began with a masters in energy engineering, from the University of Manchester in 2012. Adam is now back in Christchurch where he earned his chemical engineering degree from the University of Canterbury in 2010. Adam brings to the team a wealth of experience ranging from large corporations to small, mission-driven startups, along with management experience running a data science bootcamp, to develop, teach & mentor new data scientists.

Adam’s interest in the power of data & machine learning to solve real-world problems, with a strong focus on climate problems make him a great fit at Gridcognition, with much of his previous work focused on optimisation & control – including reinforcement learning, mixed-integer linear programming & evolutionary algorithms, along with experience in time series forecasting.

His work at Gridcognition sees him developing scalable, reliable & tested data pipelines. A few highlights from his first 6 months include:

  • Converting a manual, Jupyter Notebook based data ingestion process into an automated & tested pipeline based on AWS Lambda,
  • Building a pipeline to extract & clean electricity market data into AWS S3, with an AWS Athena layer to access data with SQL queries.


Without further ado, let’s meet Adam! 

What got you interested in the energy space?

I studied chemical engineering at Canterbury University in New Zealand, and did an internship with Fonterra in my third year as part of the degree. There was a specific energy aspect of this project (recovery of low-grade process heat) that interested me more than the actual project (optimizing the operation of a milk powder evaporator).


What’s your favourite feature you have worked on so far?

I think the work I did on data ingestion has been the most useful to the business, and so I think that’s probably top of my list – when I started, a lot of the data ingestion – load shapes, tariff info etc was processed manually by James or I, for the simulation engine, and the automation of that process removes some human error risk – and free’s us up to focus on higher value customer workflow, and trickier problems. It also gave me a chance to challenge myself with some new tech to create an automated, serverless solution.


What attracted you to work with Gridcognition?

Twitter. It’s weird because I’m not really a big Twitter user, but Gridcognition was recommended to me by the algorithm saying it was ‘someone you should follow’, so I did, and then checked out the website and saw they were looking for a data scientist. This role is a really great fit, combining energy and data science and so if you were prone to cliche, I guess you’d say the rest is history.


What is something awesome about another member of the team?

I’m really enjoying working with the founding team. I’ve worked for a few startups in the past, and it’s never a smooth ride. Both Pete and Fabian have built and run a successful startup prior to founding Gridcogntion – it’s nice to work with people who have been on the journey we are currently on. At Gridcognition we know we’re working on something important, and a strong cohort of impressive (and paying!) customers is easy validation that it’s something worth working on.


Who (outside of Gridcognition) inspires you from a work perspective?

François Chollet is one of my favourite modern thinkers. He’s a French software engineer and artificial intelligence researcher currently working at Google, and his work in machine learning is pretty inspiring. He’s a clever, philosophical guy, with a mix of hard and soft skills. He is the creator of the Keras deep-learning library and a main contributor to the TensorFlow machine learning framework. Alongside this work building deep learning tooling, he remains critical of deep learning – it’s rare to find someone who is able to directly criticize & tone down their own work – the opposite is more common!


If you’re an energy nerd like us, and you’re keen to help Adam and the team continue to accelerate the transition to a decrentralised, decarbonised energy future, check out our jobs board at

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