André Fonseca

Experienced data scientist with over 8 years in the field specialized in Statistical Analysis, Machine Learning and Deep Learning, with extensive experience in data engineering, manipulation, and modelling. Skilled in designing and implementing sophisticated AI and machine learning models and familiar with Large Language Models (LLMs) and Natural Language Ptrocessing (NLP) and vector databases for handling high-dimensional non-structured data. Proficient in Python and related libraries, such as Pandas, NumPy, Matplotlib, Scikit-Learn, TensorFlow and PyTorch with expertise in experimental design, data preparation, model fine-tuning, and hyperparameter optimization. Advanced in SQL for relational database management, providing efficient data analysis and insights.
 

Data Scientist                                                                                                                                                        01/2020 - 07/2024

Lead data scientist in charge of data cleaning, manipulation and analysis revolving on the implementation of supervised/unsupervised statistical and Machine Learning models on large datasets. Feature engineering and manipulation (i.e. dummy variable creation, feature selection) were worked upon followed by Machine Learning and Deep Learning algorithm implementation and fine-tuning (through hyper-parametrization engineering).

Responsibilities

• Designed and implemented machine learning algorithms and data preprocessing methods
• Applied Machine Learning and deep learning models such as Neural Networks for data insights 
• Conducted exploratory data analysis (EDA) and data visualization to identify trends and patterns
• Used Python (Scikit-Learn and Tensorflow) and R for large-scale data processing and model development. 

Data Scientist                                                                                                                                                        09/2017 - 12/2018

Developed and implemented data cleaning and manipulation processes, focusing on data extraction, transformation, and analysis.  Analyses were conducted based on hypothesis testing statistical methods involving both parametric and non-parametric tests and their performance accessed.

Responsibilities:

• Performed data extraction, cleaning, and manipulation using query-based methods for structured and unstructured data.
• Applied statistical techniques, including parametric and non-parametric hypothesis testing, to analyze datasets and validate model performance. 
• Assisted in feature engineering tasks such as dummy variable creation, outlier removal, and data imputation. 
• Utilized R for statistical analysis, data manipulation, and visualization.

PhD in Biotechnology and Master's in Oncobiology