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Machine Learning @SurveyMonkey
I have 10+ years of significant hands-on, technical experience in Data Science and Machine Learning Engineering across a wide range of industries. I enjoy solving problems with high quality technical solutions, focusing on Software Engineering best practices to produce clear and easily maintainable codebases and being accountable for the accuracy and scalability of models. My main focus is the R&D of projects involving Machine Learning components, and I have a proven track record of building end-to-end pipelines working across the whole ML life cycle. Proficient in Python, SQL and R, experienced with Cloud computing (AWS suite: EC2, EMR, S3, Sagemaker, Lambda / Step functions), Data Science / Machine Learning libraries (Scikit-Learn, PyTorch, Pandas, Numpy, Scipy, Matplotlib), distributed computing (Spark, Dask), Databases and DataWarehouses (Snowflake, PostgreSQL, Oracle, AWS Athena), MLOps and ML engineering / observability (FastAPI, BentoML, CML, Docker, Kubernetes, ArgoCD, Grafana). I am a result-driven individual with strong technical and interpersonal skills, able to present insights and communicate clearly with non-technical professionals and external stakeholders, managing expectations and project timelines in a reliable way. Soft skills: Collaboration, Leadership, Adaptability, Empathy, Strong work ethics, Clear communication, Project Management.
ENSAI
Ecole Nationale de la Statistique et de l'Analyse de l'Information, Rennes (France)
January 1, 2015 – January 1, 2016
Alma Mater Studiorum – Università di Bologna
Master's degree, Statistics
January 1, 2014 – January 1, 2016
Università degli Studi di Napoli Federico II
Bachelor's degree, Economics
January 1, 2011 – January 1, 2014
SurveyMonkey
Senior Machine Learning Engineer
January 1, 2023 – Present
Remote
Casavo
Senior Data Scientist
May 1, 2021 – December 1, 2022
Remote
Sisal Group
Data Science Lead
August 1, 2020 – May 1, 2021
Milan, Lombardy, Italy
Helixa
Machine Learning Scientist
August 1, 2017 – August 1, 2020
Milan, Lombardy, Italy
Machine Learning Reply
Junior Machine Learning Scientist
September 1, 2016 – August 1, 2017
Milan, Lombardy, Italy
BitBang
Advanced Analytics intern
April 1, 2016 – July 1, 2016
Bologna, Emilia-Romagna, Italy
Deep Learning
December 1, 2015 – March 1, 2016
Research project. The goal was to understand and apply Deep Learning models through autonomous bibliographic research. Application on classic computer vision benchmaek datasets with the R Machine Learning environment "h2o".
Hotel bookings analysis for Disneyland Paris
November 1, 2015 – March 1, 2016
Analysis of Disneyland Paris hotel bookings with the goal of explaining which factors have the greatest impact on the average rental income per paid occupied room (ADR, Average Daily Rate). The project was developed in collaboration with the "Market Analytics and Offer Strategy" team at Disneyland Paris. Statistical models used: ANOVA, Regression trees
Time series analysis
November 1, 2015 – January 1, 2016
Analysis and modeling of oil price (dollars/barrel) between 1988 and 2014 then forcast for 2015. Best fitting model: first-difference autoregressive for the conditional expectation and GARCH-M process for the conditional variance. Data: Oil Barrel price, period 1988-2015 Source : The Pacific Exchange Rate Service - Database Retrieval; http://fx.sauder.ubc.ca/data.html
Customer Relationship Management
October 1, 2015 – March 1, 2016
Analysed the global and CRM strategy of IKEA France through common Marketing models and CRM frameworks. I. Highlighted the “as is” model II. Proposed an improved “to be” model
Optimization
October 1, 2015 – December 1, 2015
- Linear optimization techniques to solve common Revenue Management problems (Knapsack problem, Shortest path problem) - Dynamic optimization methods such as Markov chains to mimic the customers' decision process - Stochastic optimization algorithms to model the turnover of a public transport company, and to asses the profitabilty of chartering transport mode
Scoring for the banking sector
September 1, 2015 – November 1, 2015
Database analysis to target customers with the greatest probability of moving to an upper customer segment. Application and comparison of different models/algorithms (Logit regression, CART, Random Forests) Project developed with R and SAS under the direction of Jean-Philippe KIENNER (JPK Consulting)
Samsung Digital Native Award
October 1, 2013 – January 1, 2014
Complete development of a Marketing plan for the Samsung notebook "Ativ book9 Lite".
Sequence Models by deeplearning.ai on Coursera.
Coursera
June 24, 2026 – Present
Convolutional Neural Networks by deeplearning.ai on Coursera.
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects by deeplearning.ai on Coursera.
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai on Coursera.
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning by deeplearning.ai on Coursera
Coursera
June 24, 2026 – Present
English language certificate - IELTS, grade 8/9 (Level C1)
British Council
June 24, 2026 – Present
Chinese language certificate - HSK, level A2 (Score 198/200)
Hanban - Confucius Institute
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project experience, ranging from academic research to industry applications in various sectors (banking, real estate, market research, gaming), demonstrates adaptability and a broad interest in applying data science. Their leadership roles and mentoring activities suggest a proactive and collaborative approach, which aligns well with a culture that values knowledge sharing and team growth. The continuous literature research and experimentation with SOTA architectures indicate a strong drive for continuous learning and innovation.
Soft Skills & Operational Fit
The candidate's experience in leading teams, mentoring junior members, and advocating for best practices suggests strong leadership, communication, and collaboration skills. Their involvement in architectural design and data integration strategies indicates a strategic mindset and ability to work with broad stakeholders. The focus on improving evaluation practices for LLM and agent-based projects highlights a commitment to quality and robust operational processes.