• Implemented NLP models with BERT for topic modeling and sentiment analysis on diverse textual data sources, such as Steam forum posts, game reviews, and customer feedback.
• Developed a product that integrates a Transformer-based sentiment analysis model with a Slack bot, enhancing the community management team’s response time.
• Implemented supervised machine learning models (Random Forests, XGBoost) to predict player churn.
• Applied unsupervised machine learning techniques (K-means, UMAP) and embedding models to classify players into distinct personas based on behavior and textual data, leading to changes in game development and more targeted marketing campaigns.
  
• Performed network analysis on wishlisted game titles, uncovering cross-genre player interests and informing game marketing and development strategies.
• Created custom web scraping tools using Python to extract Steam platform data (reviews, forum posts, user-owned games, user-friends lists, wishlists), supporting marketing strategies.
• Collaborated with game developers to design and implement in-game telemetry logging, capturing key player interactions and performance metrics.
• Developed interactive dashboards using Plotly, presenting KPIs and in-game metrics to various stakeholders.
• Generated reports on player behavior, game performance, sales, revenue, and market trends, informing business strategy and product development.
• Applied statistical techniques, including Multiple Regression, ANOVA, Bootstrapping, and time series analysis using XGBOOST, to get insights into various aspects of the business, such as player behavior, marketing campaign effects, sales and revenue forecasting.
• Worked closely with the marketing team to implement targeted campaigns based on player persona analysis, improving customer acquisition rates.
• Established key performance indicators (KPIs) for the company’s main games.

Link to doctoral dissertation https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-481316

• Expertise in differential equations, dynamical systems theory, matrix algebra, evolutionary game theory, and adaptive dynamics, with a focus on eco-evolutionary feedback models.
• Applied adaptive dynamics and game theory principles to analyze the interactions of biological organisms in resource competition and species coexistence.
• Presented research findings at international conferences and published in peer-reviewed journals.

Key projects

• Multi-trait Eco-Evolutionary Model: Developed a mathematical model analyzing the impact of the co-evolution of multiple biological traits on the potential for biological diversification.
• Adaptive Plasticity in Resource Competition: Developed an agent-based model to simulate the effects of phenotypic plasticity on species competition for resources.
• Life-Cycle Complexity and Biological Diversification: Developed a novel mathematical and agent-based model investigating the impact of the co-evolutionary dynamics of resource acquisition traits on biological diversification of organisms with complex life-cycles (e.g. frogs, fishes, insects).
• Life-Cycle Complexity and Community Assembly: Worked on developing a novel mathematical and agent-based model investigating the impact of complex life cycles on species coexistence and ecological community structure.

• Performed bioinformatic analyses.
• Statistically analyzed genomic data using bash, Python and R.
• Performed statistical modelling (Approximate Bayesian Computation, clustering).
• Presented research findings via oral communications in multiple conferences in Brazil.
• Wrote one research paper.
• Taught lectures in the Population Genetics course for Biology undergraduates

Education & SKills

MSc Artificial Intelligence

Stockholm University

Interrupted 2023

PHD Mathematical evolutionary biology

Uppsala University, Sweden

2022

MSC population genetics

Universidade Federal de Minas Gerais, Brazil

2015

BA philosophy

Universidade Federal de Minas Gerais, Brazil

2012