Matheus Gabriel • Data Scientist · Banco do Brasil

Engineering systems that turn data into decisions.

I build high-throughput data systems and ML workflows at Banco do Brasil, with a focus on urban mobility and predictive modeling. My work is defined by a commitment to shipping AI programs that are measurable, maintainable, and deeply integrated into complex institutional frameworks.

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Location
Brasília, Brazil
Status
Open to opportunities
MSc candidate MSc in Applied Computing — UnB Applied Computing with focus on AI and statistical methods.
Software Engineer Bachelor's in Software Engineering — UnB Rigorous background in algorithms, systems, and software design.
Banco do Brasil Technology Advisory Technology governance and data-driven decision making in Brazil's largest bank.
Multiple certifications Certifications and continuous learning Portfolio spanning Python, BI, orchestration, and language proficiency milestones, backed by continuous technical specialization.
Current Research University of Brasília (UnB) · Starting 2026

Masters in Applied Computing

Pursuing applied research at the intersection of machine learning, statistical inference, and software engineering — with a focus on systems that are measurable, reproducible, and useful in practice.

AI Statistics Software Engineering
Machine LearningStatistical ModelingSoftware EngineeringApplied AI

Academic Note

Institution

UnB

Status

Starting 2026

Projects

Work that shipped

Real programs, real constraints, and delivery choices grounded in institutional and engineering reality.

Data Scientist / Python Microservices Engineer · 2020 – 2022

ALEI 2G — Second-Instance Judicial Process Services

AI.lab UnB / CNJ

Worked on ALEI 2G in a judicial AI initiative focused on second-instance legal processes. The work centered on Python microservices, model integration, and backend services supporting AI-enabled legal workflows.

Impact

Built Python microservices to integrate AI models into second-instance judicial process workflows, enabling service-oriented delivery for legal analysis systems.

  • Python microservices integrating AI models into second-instance judicial systems
  • Service-oriented backend delivery for legal analysis workflows
  • Infrastructure for operationalizing NLP capabilities in institutional contexts
Legal TechMicroservicesNLPPython
Tech Lead / Data Scientist · Jun 2022 – Jan 2024

ALEI 1G — Legal Document Analysis Platform

AI.lab UnB / CNJ

Acted as Tech Lead and Data Scientist in ALEI 1G, applying NLP and AI/ML to first-instance Brazilian legal processes with higher data volume demands and classification workflows at national scale.

Impact

Led a squad and built NLP and classification solutions for first-instance judicial processes, operating over larger data volumes and reducing manual categorization workload for court analysts.

  • Squad leadership for delivery of first-instance legal document classification workflows
  • NLP and classification pipelines operating over larger judicial data volumes
  • FastAPI services integrating AI outputs into institutional judicial systems
NLPLegal TechClassificationMLflow
Data Scientist · 2024 – Present

Spid BB — Urban Mobility Analytics for Public-Sector Travel

Banco do Brasil

Contributed as a Data Scientist to Spid BB, building analyses over large data volumes, predictive models related to urban mobility demand and behavior, and geoprocessing workflows to support travel decisions in public-sector corporate contexts.

Impact

Worked on data-intensive analytics and predictive modeling for Spid BB, an urban mobility app for corporate travel serving public-sector organizations, with additional work in geoprocessing.

  • Large-scale analysis of mobility and travel data for public-sector corporate trips
  • Predictive models to support demand and travel-related decision making
  • Geoprocessing workflows for spatial analysis and territory-aware insights
Urban MobilityPredictive ModelingGeoprocessingSpark
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