Angelo PennatiAP
Open to opportunities

Angelo Pennati


Adorer of machine learning and expository writing.


What I'm looking for

I'd like to work on difficult and interesting problems, which are not solvable with simple queries and exploration, but rather require theory and forethought. If my technical expertise could be leveraged for a cause that benefits humanity at-large, that would be much preferred. Love open, but efficient collaboration.

I like to look at data, do fancy things with it, and explain the fancy things with simple language. I prefer having a concrete target or deliverable, but I'm also pretty into exploratory analyses and proofs-of-concept.

Passionate about Neuroscience, NLP and recommender systems.


quantilope logoQU



Sep 2021 - Present (2 years 8 months)

Developed, implemented, tested and deployed a new framework for segmentation-based typing tools for Quantilope’s web app. Led the design, development and presentation of a study on Sustainability, published by >10 sources, including Business Insider. Led the Data Science consulting on accounts & projects worth over €500,000.00, for various Global Fortune 500 enterprises.


Data Engineer

2110 LLC

Jun 2020 - Jul 2021 (1 year 1 month)

● Developed and automated a financial modeling and evaluation pipeline feeding directly into a Tableau environment.
● Tasked with technical due diligence and risk analysis on high stake investment prospects in the biotech & healthcare spaces

SPARK Neuro logoSN

Senior Research Analyst (prev. Research Analyst)


Jan 2019 - May 2020 (1 year 4 months)

Processed and visualized time-course EEG data, developing new Tableau visualizations for content > 20 minutes in length. Developed a Python framework for Eye-Tracking Area-of-Interest analysis geared towards UX and web-page studies. Prepared and presented final reports for a range of high-profile consumer brands such as Barclay’s and Netflix, with a heavy
focus on result visualization to delive



Ma Lab for Computational Neuroscience

Jan 2017 - Dec 2017 (11 months)

Closely engaged with the mathematical principles behind deep generative models, with a specific focus on both supervised and unsupervised approaches to machine learning, leveraging Keras and Tensorflow. Implemented a Restricted Boltzmann Machine suited to the probabilistic inference of co-linearity in visual perceptual organization, characterized by the novel use of a contrastive divergence model

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