Nour Elba

Created a SaaS side-project to help people find jobs using state of the art machine learning + generative AI.
Hacked together an app to help people find jobs, serving thousands of customers all around the world
Developed a proprietary ML model that can match users with jobs by analyzing over 100 statistical features
Helps job seekers find jobs that are a good fit for them and helps employers receive resumes of applicants that are already vetted to fit the profile of the job description

Launched a data science research company, applying large-scale ML solutions for enterprise clients globally, leading a team of data engineers/scientists.
Project: Predict if a tweet is going to be viral in the next 48 hours—within one hour of that tweet being posted
Result: Stitched four neural network models, achieving 90% accuracy (sMAPE). Solution is being used by a large US-based media intelligence company every day (with customers like SpaceX, BofA, Subway)
Project: Detect article relevancy for an entity based on a large corpus of labeled data (300k+ articles). Primary goal is to enable predictions for new clients which have no history/labels
Result: Implemented transfer-learning techniques to transform the data to fit a spectral temporal graph neural network, achieving an 84% F1 score for existing entities and 75% on average for new/sparse-data entities

Built an end-to-end system for operational data integration and weekly purchase recommendation reports, correlated with Brent Crude Oil futures
Led a team of 3 UCLA interns across the entire lifecycle of deploying a machine learning product/solution

Created a discounted cash flow model for rate applications and infrastructure spend assessment
Automated key data initiatives, saving significant time and resources annually
Initiated project to migrate the organization’s data strategy to the cloud, architecting an end-to-end modern data stack
Pioneered a cultural shift in data usage organization-wide through a "Data Stewarding" system