Francesco Gentile

Data Scientist

https://talent.gravityer.com/francesco-gentile

PhD student working on Mechanistic Interpretability

University of Trento

Key Strengths

Strong foundational knowledge in Python for data science and machine learning, evidenced by numerous projects.
Experience with various machine learning and deep learning concepts (e.g., Visual Grounding, Continuous-Time Dynamic Graphs, Interpretability of CLIP).
Exposure to academic research environments, indicating a capacity for theoretical understanding and problem-solving.
Familiarity with Jupyter Notebooks for data exploration and model development.
Experience with Google Cloud Platform API client for Python, suggesting some cloud exposure.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are primarily academic or personal, demonstrating initiative and a strong interest in research and development within data science. The current role as 'Data Scientist' at the University of Trento aligns well with the target role. However, the lack of diverse industry experience or collaborative team projects outside of academic settings might indicate a need for adaptation to a more corporate or product-focused environment. The breadth of skills is good within the academic data science domain but lacks explicit mention of MLOps, production deployment, or large-scale data engineering tools often required in industry.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. The psychometric test score is 0, providing no insights.

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Projects

SITH

March 3, 2026 – Present

[CVPR 2026] From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition

View Project

i2ml

February 25, 2026 – Present

Materials for the "Introduction to Machine Learning" course @UniTN 2025/2026

View Project

gdown-async

November 29, 2024 – April 28, 2025

Google Drive downloader with async support for Python

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ctdg

July 22, 2024 – September 2, 2024

A PyTorch library for Continuous-Time Dynamic Graphs

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dine-smart

July 6, 2024 – August 21, 2024

Project for the "Human-Machine Dialogue" course (2023/2024) @ UniTN

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google-cloud-platform

July 6, 2024 – November 10, 2024

Google Cloud Plarform API client for Python

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eden

May 5, 2024 – August 30, 2024

Topology-based Estimation of Distribution

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visgator-base

March 31, 2023 – April 28, 2023

UniTN Deep Learning Course Project 2023 - Visual Grounding

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deliveroo

March 28, 2023 – Present

Project for the "Autonomous Software Agents" course (2022/2023) @ UniTN

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nextapp

May 11, 2022 – June 8, 2022

Project for the "Software Engineering" course (2021/2022) @ UniTN

View Project

Key Strengths

Strong foundational knowledge in Python for data science and machine learning, evidenced by numerous projects.
Experience with various machine learning and deep learning concepts (e.g., Visual Grounding, Continuous-Time Dynamic Graphs, Interpretability of CLIP).
Exposure to academic research environments, indicating a capacity for theoretical understanding and problem-solving.
Familiarity with Jupyter Notebooks for data exploration and model development.
Experience with Google Cloud Platform API client for Python, suggesting some cloud exposure.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. The psychometric test score is 0, providing no insights.

Francesco Gentile

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Experience

Projects

Key Strengths

Cultural & Operational Fit