Nuno Castro

Director AI FDE

The AI FDE team is a highly specialised customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams.

Sr Manager Applied Science

Amazon Web Services (AWS) Amazon Web Services (AWS) Full-time · 3 yrs 10 mosFull-time · 3 yrs 10 mos Sr Applied Science Manager Sr Applied Science Manager Apr 2024 - Dec 2025 · 1 yr 9 mosApr 2024 to Dec 2025 · 1 yr 9 mos London Area, United Kingdom · HybridLondon Area, United Kingdom · Hybrid UKI, EU North, South Europe exec leader and Sr. Applied Science Manager (L7) within the Generative AI Innovation Center. Directed the scientific vision and strategic delivery of over 150+ Generative AI and Agentic customer facing engagements for AWS's largest enterprise customers. This work created significant benefit for customers and drove platform consumption.

🚀 Executive AI Strategy & GTM: Acted as a trusted advisor to C-level/VP stakeholders, transforming business objectives into AI roadmaps. Partnered with Sales and Field Engineering to influence AI use case prioritization and adoption.

🧬 Applied Science Leadership & Production Delivery: Managed a team of 20 scientists and engineers, overseeing the scientific design and production strategy for complex, multi-modal, and multi-tool Agentic AI solutions that required sophisticated evaluation (LLM-as-a-judge).

🌐 Broad Sector Impact: Delivered high-value solutions for over 150 projects across all sectors, including: Financial Services / Regulatory (stock exchange surveillance, document processing and personalization); E-commerce & Retail / Media (search, video generation, marketing content generation); Tech, Auto, & Industrial (image generation/evaluation); social media & gaming (AI voice streamer and gaming commentator); Agentic AI systems for complex operational workflows (e.g., airlines & airports, healthcare); and Foundational ML for core business functions (e.g., ML pipeline migration, security, personalization). These solutions drove significant revenue, cost savings, process automation, and new customer capabilities.

📢 Thought Leadership: Drove 30+ customer references and testimonials featured in executive keynotes and press coverage. Speaker on dozens external and internal talks such as AWS London and Berlin summits (1000+ attendees). Co-authored multiple official AWS AI/ML Blog posts on topics including Medical Content Creation with GenAI and Intelligent Document Processing.

Director of Data Science

Director ML Science for Personalization and Recommendations (4 teams, 22+ people). Director ML Science for Search and Sort, Hotels.com. Head of Data Science for EPS, the B2B Expedia brand.

Sr Data Scientist in the Analytics team

Sr Data Scientist in the Analytics team of the world largest travel company.

Data Scientist in a fraud prevention solution

Research and development in a large scale credit card fraud prevention solution, which processes 2B credit card transactions a year. I have been working as a data scientist in the fraud detection classification tools. I have also led the development of a REST API web service for helping online merchants detect fraud in their payments.

Researcher in search and indexing techniques in big data

Developed efficient search techniques in big data using Hadoop. The goal was to retrieve the Top-K nearest neighbors to the query sequence, for N queries at the same time. I also implemented a state of the art index for very fast approximate search.

PhD in Machine Learning and Computer Science

Research and Development of highly scalable pattern discovery algorithms for Terabyte sized disk-based or streaming data, and statistical evaluation measures for pattern discovery algorithms, published in top-tier conferences. One of the approaches won the Google-sponsored best student paper award and was also published in a journal.

Research and Development in a telecommunication networks analysis product

Research and Development in a leading telecommunications network management and analysis product (SPOTS). The SPOTS is implemented in more than 90 countries at top telecommunication mobile operators (e.g. Vodafone,T-Mobile). After just 1 year, I was leading the online monitoring subsystem. This subsystem monitors thousands of network objects properties simultaneously, triggering alarm events in case anomalies are detected. I was also responsible for the product’s System Monitoring tool, and performed the research, analysis, and specification of Adaptive Thresholding features for the Real Time subsystem. Main technical skills covered: Java, C++, ClearCase, unix shell scripting, machine learning.

Computer Science and Systems Engineering degree

During my 5 year undergraduate degree at the University of Minho I became well versed in programming in Java/C/C++/Perl/VB/SQL/PHP/HTML/Haskell/Prolog, artificial intelligence, machine learning, statistics, databases, object oriented programming, UML, software engineering, web programming, networks, data structures and algorithms, computation theory, cryptography, GUI design, XML/XPath/XSL, distributed programming, operating systems and computer architectures. My efforts were recognized by winning two university merit awards in 2003 and 2004. I spent a semester abroad at the Utrecht University (The Netherlands), where I attended courses and performed projects supervised by Doaitse Swierstra. I spent my last semester performing research in event forecasting at Siemens as an intern, where my project achieved a grade of 19/20.

Nuno Castro

Expedia Partner Solutions is using deep learning models to improve the hotel booking process for partners and - ultimately - the end consumer.

Nuno Castro

Don't miss out on joining our Data Science Director, Nuno Castro at #ITBBerlin talking about how #machinelearning can drive your business’ success at 10.30am on the 7th March in the eTravel Lab, Hall 6.1 https://t.co/prG8i2x2f8 #datascience #travel #tech pic.twitter.com/tJuoZ1aIia

— Expedia Partner Solutions (@expediapartners) March 5, 2019

Nuno Castro speaking at #ITBBerlin “Through EPS’s API, partners can unlock the power of Expedia Group and get access to our machine learning” #travel #epsrapid #api

Machine learning is something we hear a lot about – we know it’s going to make our lives easier and our businesses more successful but how can you implement it in your businesses today? Learn how machine learning can easily be part of your offering – no data science skills needed!

Nuno Castro

With the rise of big data comes the need for more highly skilled people to mine and interpret that data for businesses. This is the role of a data scientist, the job that Harvard Business Review called "the sexiest job of the 21st century" back in 2012.

Nuno Castro

PyData London 2017 talk where I cover how Expedia has been ranking hotel images using deep learning.

Nuno Castro

"Chatbots have recently received a great deal of attention from both industry and academia. Cutting edge advances in deep learning (namely LSTM) have enabled these developments. In this talk, we will share how Expedia Affiliate Network is using a chatbot built using deep learning to manage a hotel search conversation with consumers."

Nuno Castro

"We all need to make a good first impression. Even hotels. That’s why travel giant Expedia is using AI to help hotels put the right photos in front of the right people. Travelers spend less than a second deciding what they think of a place. So those pictures better be Instagram-worthy..."

Nuno Constantino Castro and Paulo J. Azevedo

Extends the co-winner of the Google sponsored best student paper award from SDM’11. This paper gives a method to evaluate statistical significance of discovered patterns in time series, enabling ranking and filtering of the often large number of patterns discovered by time series data mining techniques. In addition to additional details on the algorithms, this paper includes additional results.

Nuno Constantino Castro and Paulo J. Azevedo

An approach for assessing (for the first time in the literature) the statistical significance of time series patterns. Statistical significance tests are used to assess each pattern’s p-value. [Best Student Paper].

Multiresolution Motif Discovery in Time Series

A highly efficient algorithm for pattern discovery in time series data. The algorithm finds all patterns in the database in linear time: uses one single sequential scan over the database; and allows adjusting the amount of memory to use using a clever space saving approach.