From 10+ years in sales to machine learning.
I'm completing my MSc in Computer Science, specializing in Data Science and Business Informatics, at the University of Pisa. I build models that work on real data and explain their decisions, from optimization and NLP to big data and business intelligence.
A fresh start: new city, new field, same determination.
I spent more than ten years in sales: on the phone for brands like Vodafone, Eni and Fastweb, and on the shop floor at GameStop, Foot Locker and others. It taught me to listen, to negotiate and to turn a skeptical customer into a convinced one. But deep down I knew it was not what I wanted to do with my life.
So I changed it. At 34 I moved from Bari to Pisa and started again from scratch, to devote myself to my real passion: computer science and machine learning. I earned my BSc in Computer Science at the University of Pisa, with a thesis on an original decision tree algorithm written from scratch in Python.
Today I'm completing my MSc in Computer Science, specializing in Data Science and Business Informatics, and I'm looking for a team where both halves of my story count: the rigour of the models and the ability to explain them to the people who use them.
Ten years in sales taught me what many technical profiles miss: explaining complex things simply.
Problem, approach and result, with the numbers from each repository. Everything is open source.
A custom interior-point solver that beats general-purpose tools by exploiting the structure of the problem.
Constrained Lasso is a core sparse-regression problem, but general-purpose solvers like OSQP and SCS treat it as just another quadratic program.
Derived the quadratic reformulation and KKT conditions, then implemented Mehrotra's predictor-corrector method, reducing each Newton step to a structured positive definite system.
Up to 264.9× faster than CVXPY baselines, with near-identical solutions, on datasets of up to 2,000 features.
My largest project: tabular and temporal data mining on the whole IMDb catalog, from anomalies to explainable predictions.
149,531 heterogeneous IMDb records and 1,134 box-office revenue curves: skewed features, rare classes and patterns hidden in time.
Robust scaling with Yeo-Johnson, consensus anomaly detection with three detectors, imbalanced learning, tree ensembles and a regularized neural network explained with SHAP; DTW clustering, matrix profiles, shapelets and MiniROCKET on the time series.
A 9-notebook, 32-page study: 1,089 anomalies confirmed by consensus, rating models explained feature by feature, and box-office trajectories grouped into distinct commercial lifecycles.
Can the sound of a track explain its genre and its popularity? An end-to-end answer on 114,000 songs.
114,000 tracks spread over 114 overlapping Spotify genres, with popularity that no single audio feature explains.
A hand-built taxonomy from 114 to 10 macro-genres, LOF anomaly detection, K-Means validated by a 4.6M-parameter deep network, tree ensembles, SMOTE and SHAP.
97.5% cluster recovery, 0.90 macro ROC AUC on 10 genres, 18× the R² of linear models and +59% recall on the rare explicit class.
Four generations of NLP on the same half-million reviews, compared fairly.
568,454 Amazon reviews: how far do classic methods go, and what do deep learning and transformers really add?
A balanced study set of 164,074 reviews; Naive Bayes and linear SVM on TF-IDF, a custom 2.66M-parameter CNN-BiLSTM on Word2Vec trained on the corpus, and DistilBERT.
ROC AUC rises from 0.96 to 0.98, with detailed error analysis and precision-recall evaluation for every model.
49 years of global seismic data, processed in a distributed way, without throwing information away.
3.27 million seismic records from 1970 to 2019, with key variables missing in up to 41% of rows.
Regression-based imputation in Spark instead of dropping columns, K-Means on standardized features and on geography, Random Forest magnitude classification with threshold analysis, Folium maps.
A clean catalog of 2,607,588 earthquakes and clusters that isolate deep events down to 735 km and remote, poorly covered regions.
My BSc thesis: a decision tree that splits the way the data actually is.
Popular libraries build binary trees and need categorical data to be encoded into numbers first.
An original classifier written from scratch: native categorical attributes, multiway splits, three strategies for numerical data, two impurity criteria and pre-pruning.
Benchmarked against scikit-learn on 10 datasets and 51,840 recorded evaluations, with comparable peak accuracy, higher on two datasets. Documented in a 75-page thesis.
def _fit(self, data: pd.DataFrame, features: list[str], current_depth: int) -> "MixedDecisionTree.Node": """Grow the tree recursively, one multiway split at a time, until a stopping criterion is met.""" node, stop = self._stoppingCriteria(data, features) if stop: return node # choose the attribute with the highest information gain gain_dict = {f: self._getGain(data, f) for f in features} best_split_attribute = max(gain_dict, key=gain_dict.get) remaining_features = [ f for f in features if f != best_split_attribute] root = MixedDecisionTree.Node() root.split_condition = best_split_attribute root.gain = gain_dict[best_split_attribute] root.depth = current_depth # multiway split: one child for every attribute value for value, sub_data in data.groupby(best_split_attribute): child = self._fit(sub_data, remaining_features, current_depth + 1) child.split_value, child.parent = value, root root.children.append(child) return rootdef _getGain(self, data: pd.DataFrame, split_attribute_name: str) -> float: """Information gain of a multiway split on an attribute.""" I_parent = self._get_impurity(data[self.class_name]) # weighted impurity of ALL the children of the split column = data[split_attribute_name] groups = data[self.class_name].groupby(column) weights = groups.size() / len(data) I_child = sum(w * self._get_impurity(labels) for w, (_, labels) in zip(weights, groups)) # clip tiny negative values due to floating-point error return max(I_parent - I_child, 0.0)def _get_impurity(self, attribute: pd.Series) -> float: """Entropy or Gini index of a column of class labels.""" _, counts = np.unique(attribute, return_counts=True) p = counts / counts.sum() if self.criterion == "entropy": return float(-np.sum(p * np.log2(p))) if self.criterion == "gini": return float(1.0 - np.sum(p ** 2)) raise ValueError("criterion must be 'entropy' or 'gini'")All my public projects, filterable by area. Click a row to open the repository.
The main projects in this area are in Selected work above.
The full picture, in the order that matters most to me: what I studied, what I built, what I learned along the way.
Hands-on machine learning on real, large-scale data. Every project is open source on GitHub.
Designed and built systems and networking software on my own initiative, beyond what courses required: a multithreaded client-server collaboration platform in Java (TCP, RMI, UDP multicast), a multi-process supermarket simulator in pure C (POSIX threads, sockets, signals), thread-pool and non-blocking NIO servers, and an interpreter in OCaml. All published open source on GitHub.
More than ten years in sales, on the phone and on the shop floor, before becoming a computer scientist and data scientist. Every day was a negotiation with real people, real objections and real targets.
What I took from it: listening to people, persuading with clear arguments, explaining complex offers simply and staying effective under pressure. Skills I now bring to data work.
Ten years of selling taught me to talk to anyone. I bring that to data work: I can turn a model into a story that a manager, a client or a non-technical team can act on.
Available now, open to relocation and travel. The fastest way to reach me is my Proton Mail address:
giuseppe [dot] muschetta [at] pm [dot] me
My phone number and degree certificate are available on request.