I am a Data Science professional that can deliver solid engineering solutions across many areas of the Data and AI spectrum. I can work as a Data Scientist, Data Engineer or Machine Learning Engineer, but more importantly, I bring experience from each of these disciplines to be able to accomplish end-to-end Data Science solutions. From developing Machine Learning algorithms to creating complex, Big Data-ready ETL/ELT pipelines to deploying Machine Learning as a Service, I can take a project from idea to API and deploy this to the cloud.
I am currently the Lead Machine Learning Engineer at OutMatch after the successful acquisition of Launchpad Recruits in September 2020. At OutMatch I lead the engineering division of the Data Team where I take care of very large datasets including video, review data, platform events, etc. applying multiple big-data and machine learning techniques to help talent and hiring managers make faster, more consistent and fairer hiring decisions.
At Launchpad, and previously at Intern Avenue, I worked in multiple Data Science projects, involving image processing and computer vision techniques, NLP, statistical analysis, sentiment and personality insights and data visualisation amongst others.
Specifically related to data engineering I work setting up scalable data stacks and pipelines requiring knowledge in infrastructure (Linux, containers and VMs), databases (from classic relational to distributed databases), distributed processing engines (Apache Spark), cloud deployments (CI tools, AWS) and dashboard/notebook tools (Jupyter, Apache Zeppelin), mainly using Python at the algorithm and application level, but adapting to the platform demands.
Previous to my Data Science/Engineer roles I obtained my PhD from Imperial College London, where I worked within the BICV group in computer vision, image and signal processing, machine learning and pattern recognition.
My PhD thesis focused on the design and development of appearance-based algorithms for extracting distinctive information for two main computer vision applications: object recognition and visual localisation. My main contribution to the field is a biologically-inspired visual localisation algorithm that uses appearance-based methods to model place cell behaviour and artificial neural networks to estimate locations.
Previously, I graduated from Imperial College London (MSc Biomedical Engineering), and from the University of Seville (MEng Electrical and Electronic Engineering) with 1st class honours.