Data Science Festival - Bristol

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Event details
📆 Date: Wednesday 4th November 2026
⏰ Time: 6:00 – 9:00 PM GMT
💡 Topic: Inside tem’s Data Stack: Building Data for a Changing Energy Market
🗣️ Speakers: Antonio Alvarez Caballero and Matt Bailey

tem runs the transaction infrastructure that replaces the wholesale energy market for businesses and generators. Underneath it sits one shared portfolio dataset that has to serve near real time pricing decisions and fully reconciled finance and regulatory reporting at the same time.

Join us for an evening at Battersea Power Station, where tem’s London office is also based, exploring how that actually works. The evening will feature a keynote from tem’s data leadership, a panel with the practitioners who build and run the stack, and networking over food and drinks.

Check out the full session details below!

KEYNOTE SESSION
Title and abstract coming soon!

PANEL SESSION
One Portfolio, Many Masters: Who Owns the Truth?
tem’s pricing, risk, finance and regulatory teams all work from the same portfolio data, and each needs something different from it. Pricing wants answers in minutes, finance wants fully reconciled numbers, and regulation wants an audit trail.
Our panellists build and run that stack, and they will dig into the trade offs: fast vs right, who owns the truth when definitions conflict, and what breaks when one dataset serves everyone.

Schedule:
6:00 PM – Doors open, food, drinks and networking
6:45 PM – Welcome from Data Science Festival
6:50 PM – Keynote session
7:20 PM – Short break
7:30 PM – Panel discussion
8:10 PM – Drinks and networking
9:00 PM – Event close

Please note: Due to expected demand and limited capacity, this event will be run as a ballot. The exact venue location will be shared with successful applicants when tickets are issued.

Applying for the event does not guarantee a ticket. Applications will be reviewed on a rolling basis. If you have not received a ticket by Thursday 29th October, unfortunately your application has not been successful.

Please note that adding the event to your
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You can find out more about how our ballot works in the DSF Ticketing 101.

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💡 About the event:

Join us online on Thursday 26th November for our next session with dunnhumby, featuring Marco Brambilla, Research Data Science Specialist, as he explores how data scientists can add greater detail to forecasts without rebuilding forecasting systems from scratch.

In retail analytics, forecasts are produced at scale. Senior stakeholders often want them broken down further by shopper type, demographic group, or other business dimensions. How can data scientists provide this extra detail? Should they build more granular models, rely on historical averages, or use a different approach?

Photography faces a similar challenge. Optical zooms capture genuine detail at a cost. Digital zooms are cheaper but lose detail. Modern AI-enhanced zooms can create sharper, more detailed images without more expensive hardware.

Can forecasting be approached in the same way?

In this session, we use the analogy of optical zooms, digital zooms, and AI-enhanced zooms to explore three approaches to adding detail to forecasts. You will leave with a practical framework for understanding when simple methods are sufficient, when additional modelling is needed, and how greater forecast detail can be achieved without rebuilding forecasting systems from scratch.

🎤 Speaker:

Marco Brambilla, Research Data Science Specialist @ dunnhumby

Research Data Science Specialist with 18 years’ experience at dunnhumby, designing and delivering scientific solutions that generate automated demand forecasts at scale for leading grocery retailers. For 10 of those years, I managed and developed data science teams while continuing to lead work on pricing, forecasting, and the measurement of intervention effects. My work has focused on the science and tools that support commercial decision-making and large-scale analytics capabilities.

I believe that strong scientific foundations are essential for building predictive modelling solutions that are both practical and scalable. A principled approach improves prediction accuracy and interpretability. It also makes solutions easier to extend, integrate, and operationalise while maintaining consistency across different applications.

My technical interests include mathematical modelling, statistical inference, and causal reasoning. I hold BSc and MSc degrees in Computer Science, specialising in Complex and Intelligent Systems. I subsequently completed a PhD and postdoctoral research in Computational Neuroscience.

🎟️ CLICK HERE TO REGISTER FOR FREE! 🎟️

Registering for the event: Click the ‘Register here’ button using the link provided. Once you have completed the registration form, you will receive an email containing your unique webinar joining link. You will also receive reminder emails one week, one day, and one hour before the event.

🚨 PLEASE NOTE: Clicking ‘Attend’ on Meetup does not register you for the event. You must register using the link below to receive your webinar joining link. Without a joining link, you will not be able to attend.