Jordi is a skillful Software Engineer with 18 years of software development experience for global companies, including Amazon and BBC. He has acquired the skills and expertise to build scalable startups that solve real-world problems, taking advantage of the newest technologies and development tools. Jordi is highly-skilled in Python, Java, and related frameworks, having worked extensively with a lot of different technologies. He has knowledge of systems engineering, architecture, and analysis, machine learning and neural networks, full-stack web development, as well as soft skills, including business and team management.
Worked as part of the Audience Data team to provide datasets of audience consumption of BBC content to the whole BBC, as well as registering events such as when a user reacts to a piece of content.
Involved in the delivery of the BBC’s services such as iPlayer, News, Sport, CBBC, Weather, and Radio.
Closely collaborated with team members on identifying issues and model program solutions.
Worked on the development, design, and implementation of running services for multiple projects.
Developed XOD, a project that machine learning to extract data from arbitrary websites into fields.
Involved in the creation of a service that collects data such as price from commerce sites so that the clients (often product competitors) are able to monitor.
Software Developer Engineer II - Core Machine Learning
2017 - 2017
Berlin , Germany
Worked on implementing, testing, deploying, and monitoring AWS services for multiple internal teams.
Optimized the core by ∼50% in running time by parallelizing code, replacing mathematical routines by faster aproximations, replacing java libraries by native C libraries (BLAS), among other smaller optimizations.
Assisted with the SageMaker service to build, train, and deploy scalable machine learning models for data scientists.
Co-founded a software startup that develops a unified global API for grocery shopping in local supermarkets. It provides product recommendation and basket prediction using machine learning.
Main role was to implement, test, deploy and monitor Microservices that provide an API for multiple tasks.
Implemented Product to Ingredient mapping using NLP, Product recommendation using a Hybrid of Content-based and Collaborative filtering, – Product recommendation using the Apriori algorithm, Next basket prediction using Recurrent neural networks.
Worked on developing back-end features for two start-up companies, Pipetop and Pleo, through the biggest startup incubator in the Nordic countries.
For Pleo, I implemented, tested, deployed, and monitored Microservices that provide an API for the following tasks: fraud detection, classifying transactions into a set of 7 categories, and detecting inquiry transactions.
For Pipetop, I built a content-based (web content) recommender system using machine learning algorithms that suggests potential leads to pipetop users. (AUROC performance over 97%). Also built a web classifier using a SVM trained with over 700,000 corporate websites annotated with their linkedin profile category (industry sector).
Acted as a full stack developer for an intelligent booking system used by restaurants. Built a system to automatically generate marketing campaings for our customers by automatically generating the keybords to bid on Adwords.
Lead developer for a Microservice to detect the type of cuisine of a restaurant based on the words used in the restaurant website.
Solved the table assignment problem with a novel algorithm.
Proposed and developed text mining and sentiment analysis software to address research questions related to gender diversity in corporate governance for the Copenhagen Business School.
Developed software for Columbia Business School that address research questions within various business topics, among: Modeling complex economic networks dynamics with simulations, gender equality in corporate boards, executive compensation and risk in the financial services industry using time series data.
Performed the analysis of the global network of corporations using data collected by a large team of researchers for INSEAD Business School. I proposed and implemented solutions to problems and authored sections of a book published by MIT Press.
Worked for Atos, one of the largest information technology consulting firms in the world. Acted as a Frontend and Backend developer in a team of 10 developers responsible for the development of a management system for the Barcelona City Council.
Acted as a Full Stack Developer for Management Systems Solutions, a company specialized in professional content translation, localization and creation for global businesses.
Managed to solve issues affecting production, stage, and development environments as requested.
BBC iPlayer is a platform where users can watch BBC programs online. UK users can enjoy BBC iPlayer on a computer, phone, tablet, smart TV or games console using the BBC iPlayer app (connected TV, mobile, and tablet only) or the BBC iPlayer website. Acted as Software Engineer for the project, delivering back-end features for the service using mainly Python and Java technologies. Also worked as part of the Audience Data team to provide datasets of audience consumption of BBC content to the whole BBC.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high-quality models. SageMaker provides all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at a lower cost. Acted as Software Engineer for the service, building, training, and deploying scalable machine learning models for data scientists using Python and Scala.
The project is an empirically rich study of the influence of social networks on corporate governance across countries and the emergence of a new transnational community. The chapters examine, among other topics, the surprisingly heterogeneous network structures that contradict the common belief in a single Anglo-Saxon model; the variation in network trajectories among the formerly communist countries including China; signs of convergence in response to the common structural breaks in Europe; the growing structural power of women due to gains in gender diversity on corporate governance in Scandinavia; the “small world” of merger and acquisition activity in Germany and the United States; the properties of a global and transnational governance network; and application of agent-based models to understanding the emergence of governance. Worked as a Research Assistant for the project, mainly writing software in Python and C++.
Education
Machine Learning - Stanford University; Mining Massive Datasets - Stanford University; Hadoop Platform and Application Framework - University of California; – Neural Networks and Deep Learning - deeplearning.ai; Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization - deeplearning.ai; Structuring Machine Learning Projects - deeplearning.ai; Foundations of Convolutional Neural Networks - deeplearning.ai; Sequence Models - deeplearning.ai.
Coursera
Master of Computer Applications, Artificial Intelligence
The National University of Distance Education (UNED)
Bachelor of Applied Sciences, Computer Engineering
