Machine Learning Engineer
San Francisco, California
Data Scientist with a specialization in the design and implementation of deep/machine learning algorithms. Industry experience in: predictive modeling, optimization problems and simulations.
Part of the initial Square Capital Data Science team that was tasked with the continual optimization of all lending product design, marketing strategies and servicing techniques.
Hired as Framed Data's principal research scientist with the sole task of improving the company's churn prediction algorithms for each customer.
Summer Data Science internship which helped in the uncovering of interesting patterns in flight sensory data to help with our flight operation quality assurance reporting to customers.
One year placement at NCR (EMEA HQ) as part of the BSc. (Hons) Sofware Engineering course at the University of Portsmouth.
Best Overall Student Performance Award: Prestigious award in recognition for best student performance across all degree modules.
Research conducted at Square Capital for the purpose of automatically suggesting solutions to customer email inquiries. The research yielded a novel deep learning architecture that combines two disparate data sources when estimating its predictions.
Initial research performed for Framed Data as part of the MSc Data Science Degree dissertation at Lancaster University. The research work conducted proved that Deep Learning can be successfully applied in the field of customer churn prediction by yielding better prediction results while also bypassing the tedious feature engineering phase in a traditional machine learning pipeline.