About

Soledad Galli is a Lead Data Scientist with over 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for Insurance Claims, Credit Risk, and Fraud Prevention. Soledad has also led the implementation of Machine Learning across businesses and mentored data scientists while recruiting members for the data science team. She has helped companies with brand image and participated as a speaker at data science meetings. Soledad has created multiple online courses on machine learning for a prestigious MOOC platform and developed and maintains a Python open-source project. While in academia, she secured funds for research, published 10+ scientific articles, and communicated results to scientists, patients, and journalists. Soledad is passionate about supporting others to learn more about data science, machine learning, and programming in general and using technology and technical tools and skills to support organizations working on social problems. Soledad has technical expertise in SQL, R, and Python (including Numpy, Pandas, Scipy, Matplotlib, Scikit-learn, and Keras for deep learning). She has good knowledge of object-oriented programming, unit testing, documentation, and production code for model deployment. Soledad is an expert in inferential statistics, including probability distributions, multiple comparisons using ANOVA, adjusted p-values, and non-parametric tests (e.g. Kruskal-Wallis), power and sample size calculation, and bootstrapping. Soledad is also an expert in linear and logistic regression, clustering, classification and regression trees, ensemble models (i.e., random forest, adaboost), nearest neighbors, SVMs, and neural networks (including Feed forward, recurrent/LSTM, and CNN). She has good knowledge of unsupervised models (i.e., one-class support vector machines and isolation forests) and machine learning model interpretation. Soledad has experience in NLP. Soledad is currently working as a Lead Data Scientist at LV=, where she has researched, developed, and put into production machine learning models for Motor Claims and Fraud Prevention. She has also mentored data scientists and supported in the recruitment of members for the data science team. Soledad has previously worked as a Data Scientist at Zopa and Capgemini, where she built machine-learning models for credit risk, fraud, and collections, and generated insight and designed solutions through open-source software and algorithms following briefs from clients.