About Me

About. Hackathons. Certifications. Papers. Fun.

About

I am a self-motivated, enthusiastic team worker with a keen interest in Machine Learning and Data Science. I am passionate about solving business problems through data storytelling and helping make jobs better and more efficient by understanding and supporting the organizational direction. I enjoy being strategic with data and I excel at realizing what needs to be acquired to increase the value of the existing data.

My understanding of the importance of data evolved during my time in the digital marketing industry where my responsibility was to measure and track the customer journey touchpoints involved in an online event. My MSc in Business Analytics at Imperial College Business School developed my statistical, operations research and machine learning techniques aimed at solving business problems and obtaining actionable business insight from data. My work as a Data Scientist in the FinTech, Oil & Energy and Digital Media SaaS industries has allowed me to explore how DS and ML methodologies can be leveraged to add significant business value by analyzing, mining and exploiting available information.

I enjoy working directly with clients and establishing meaningful and transparent relationships in order to foster mutual growth. I love learning about what makes an individual business succeed and its business model in order to see where I can help.

TL;DR: I'm extremely interested in implementing AI, Data Science and Machine Learning to approach difficult problems and uncover patterns in data. Helping leaders understand what questions they are not asking, but should be asking! 🔎 📈

Hackathons

AI Hack 2020 (1st Prize)

During the last weekend on February I took part in the largest student-led AI Hackathon in the UK. This third ever AI Hack aimed to bring the most forward-thinking and creative student data scientists to solve some of the world's most pressing challenges. I tackled the 24-hour hackathon at Imperial College London and was awarded first prize for the discoveries in the Airbnb Correlation One Challenge! Analysed over 40,000 Airbnb listings in New York City to understand how a listings features affected its nightly price. Explored patterns in the rentals industry and how the they relate to economic, demographic, and geographic trends at large. Created machine learning models that were able to predict a monthly revenue investors could expect from their Airbnb property. This was then used to compare yields from other property rental types so that a potential investor could make a data-driven decision on what to invest in.

COVID-19 Recovery Challenge

Took part in the 5-day innovation hackathon for all bright minds from LSE, Imperial College, Harvard, UCL and ITESM who wanted to help build a better future during these times. The aim of the event was to facilitate the generation of innovative solutions to the challenges engendered by the pandemic by providing 5 days of industry-leading talks, mentorship, networking, guidance all through an online community. This was not a traditional tech hackathon but one which gave the freedom to come up with new products, business models, policies or campaigns trying to help individuals and businesses recover post the crisis.

Certifications

Data Scientist with Python

This 23 course and 88 hour DataCamp career track helped me reinforce my importing, cleaning, manipulating, and visualizing data—all integral skills needed in the data science world. Through interactive exercises and 6 hands-on projects I took advantage of many Python libraries, including pandas, NumPy, Matplotlib, Seaborn, scikit-learn and Keras. I worked with real-world datasets, counting a Super Bowl ad dataset and a Nobel Laureates database to learn the statistical and machine learning techniques needed to train decision trees, support vector machines and artificial neural networks and use natural language processing (NLP) along with image categorization. This course has helped me grow my Python skills, and has helped me become a more confident data scientist by providing me the skills to complete the projects in my portfolio.

Machine Learning Scientist with Python

This 23 course and 93 hour DataCamp career track helped me augment my Python programming skill set with the toolbox to perform supervised, unsupervised, and deep learning as a machine learning scientist. I mastered how to process data for features, train models, assess performance, and tune parameters for better performance. The course contained various natural language processing, image processing projects, completed with popular libraries such as TensorFlow, Spark and Keras.This course has helped me improve my machine learning skills, and has helped me become a more confident programmer by providing me the tools needed to complete the projects in my portfolio.

Machine Learning Fundamentals with Python

This 5 course and 20 hour DataCamp skill track helped me practise the fundamental concepts in Machine Learning. scikit-learn was used throughout this certification to build predictive models, tune their parameters, and determine how well they will perform with unseen data. SciPy was levereaged to cluster, transform, visualize, and extract insights from unlabeled datasets, and build a recommender system to recommend popular musical artists to users. A school budgeting case study was used to build a baseline model before preforming natural language processing to prepare the budgets for modeling. Deep learning with the Keras 2.0 library was implemented in order build deep neural networds and optimize them with backward propagation and keras fine-tunning. This course has helped me grow my Python and Machine Learning skills, and has helped me become a more confident data scientist by providing me the skills to complete the projects in my portfolio.

Papers

Assessing Customer Churn Using Machine Learning Models in the Oil & Energy SaaS Industry

Supervised by Dr. W. Wiesemann. Examined the client renewal process for Aucerna and provided a probabilistic classifier that output probabilities based on a client not renewing its contract for its original amount. Identified active customer churn which is detectable by analysing hidden patterns in customer account data, payment behaviour and history data, product and solution usage data and the business-to-customer communication. Enhanced the risk assesment process by introducing a data-driven churn score. Collaborations with Aucerna.

Economic Analysis on the Profitability of a Horizontal Multi-Stage Hydraulic Fracturing well in the Duvernay Formation

Supervised by Dr. D. Stupples. Project titled: ‘Economic Analysis on the Profitability of a Horizontal Multi-Stage Hydraulic Fracturing well in the Duvernay Formation’. Examined the economic viability of a 20-year lifespan petroleum extraction site in Alberta, Canada by investigating potential production of shale oil deposits. Oil production data analysed and forecasted using MATLAB and Simulink. Collaborations with 3ESI-Enersight.

Fun

I was born in Barcelona and I grew up in Canada. Canada provided me with the richest outdoor experiences I’ve lived and consequently I love any escape to nature. In the summer, holidays are filled with hikes in the Alps and Pyrenes and the winter substitutes the dirt and rock for white snow. I absolutely enjoy shredding some pow with friends and recouping all the calories expended with powerful food after the ski day.

I like to keep active throughout the year in order to feel strong and my best. I enjoy hitting the gym and lifting weight because it is a great way to see how your body progresses and improves over time. I compliment these workouts with runs and when I ever have a partner to play with, a couple of games of squash! I started playing about 3 years ago and have fallen in love with the sport. Hit me up if you are down for a game because I am always eager to play new people!