DAIM: Inside The Algorithm | Network Forecasting & Data Science Leadership with Dr Judit Guimera Busquets

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This week, Dr Judit Guimera Busquets, Head of Data Science at Datasparq, joins Dr Jeremy Bradley to trace the journey from her PhD on air traffic network forecasting through to leading data science teams delivering real-world AI projects.

Judit explains why forecasting inside a complex network is fundamentally different from standard demand prediction: when a single airport pair is removed, the cascade effect ripples across an entire system. She walks through the multi-stage modelling framework she developed, covering city pair demand generation, network evolution, itinerary assignment, and long-term scenario planning.

The conversation then turns to what actually happens when structural shocks like a pandemic break a model's core assumptions and why human-in-the-loop design is not optional. Judit also sets out what she looks for in data scientists: pragmatism over perfection, simplicity over complexity, and a production-first mindset from day one.

She closes with her view on where applied AI is heading, including the rise of small, fine-tuned specialist models and why AI governance remains the most overlooked challenge in the field.

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If you enjoyed this episode, why not check out the Data & AI Mastery episode with Richard Masters, VP of Data and AI at Virgin Atlantic. You will learn more about how the airline leverages AI and data-driven strategies to enhance operations, optimise pricing, and deliver premium customer experiences:

Apple: https://podcasts.apple.com/gb/podcast/mastering-data-ai-insights-from-virgin-atlantics-vp/id1779783413?i=1000697801419

Spotify: https://open.spotify.com/episode/1MY3AZCvDr5eS1HlfBudHy?si=ca5ec9b2fe6d44b1

YouTube: https://www.youtube.com/watch?v=DQ3mTwTzJvA

Glossary Terms

Hub-and-spoke Model: a centralised organisational architecture where a central core connects to multiple peripheral nodes. Traffic, communication, or inventory flows through the hub rather than directly between spokes.

Network Theory: a multidisciplinary framework used to analyse complex systems by representing them as mathematical graphs

Econometrics: the application of statistical and mathematical models to economic data

Human-in-the-Loop: a collaborative AI approach where humans actively participate in an automated system's training, refinement, or operation.

Linear Regression Model: a fundamental statistical and machine learning algorithm that models the relationship between a dependent variable and one or more independent variables by fitting a straight line to the data.

Chapter Markers

(00:00) - What makes network forecasting different from standard demand prediction

(05:54) - How historical data fails when the network itself evolves

(10:05) - Modelling link addition and removal as classification problems

(13:26) - Designing for medium and long-term policy evaluation, not daily operations

(17:57) - What happens to a model when a structural shock like a pandemic hits

(22:22) - Human-in-the-loop: adjusting elasticities and running what-if scenarios

(27:20) - What great data scientists actually look like in a consulting environment

(30:03) - Getting stakeholders to use AI: champions, end users and change readiness

(32:00) - Where applied AI is heading: small specialist models and the governance gap

Useful Links

Connect with Dr Judit Guimera Busquets on LinkedIn: https://uk.linkedin.com/in/judit-guimera-busquets-696ab74a

Learn more about Judit’s PHD here: https://openaccess.city.ac.uk/id/eprint/24689/1/Busquets%2C%20Guimera.pdf

For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley

Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com