Francisco Mendes

Francisco Mendes is a Former Data Scientist currently pursuing his graduate studies, with a solid academic foundation in mathematics, statistics, and economics.

He has a wealth of experience working directly with clients across various industries, specializing in implementing machine-learning algorithms in Heavy Engineering sectors like Oil & Gas, Defense, and Space.

Francisco excels in guiding clients through the entire algorithm life-cycle, from understanding the business problem to developing, explaining, implementing, and maintaining the algorithms.

His expertise includes conveying complex statistical and mathematical concepts to diverse audiences, showcasing exceptional presentation skills.

Beyond his professional endeavors, Francisco has a keen interest in art, philosophy, and literature, demonstrating an affinity for complex numbers.

His interest areas span signal processing, spectral analysis, time series, Markov models, and matrix completion methods, particularly focused on recommendation engines and noisy image completion.

In the realm of Deep Learning, Francisco brings competencies in DNN, CNN, LSTM, and utilized pre-trained models like VGGish for acoustic classification and BERT for text classification and embedding.

Moreover, his implementation skills encompass AWS, enhancing his ability to deploy solutions efficiently.

Francisco pursued his education in Economics at the University of Chicago and furthered his studies with a Master's degree in Statistics and Quantitative Economics from the Indian Statistical Institute, Kolkata.

Professionally, he currently serves as a Graduate Student at the University of Chicago, having previously held roles as a Senior Data Scientist, Consultant, and Intern at Deloitte India (Offices of the US).