Pedro Tabacof
Principal Machine Learning Scientist at Intercom
Ireland
Summary
🤩
Rockstar🎓
Top SchoolPedro Tabacof is a Staff/Principal machine learning scientist based in Dublin with 11 years of experience building and shipping ML systems that moved the needle at three unicorns (Wildlife Studios, Nubank, iFood) and scaling an early-stage startup sixfold. A computer engineer by training with an M.Sc. in Deep Learning and research stints at Motorola and Accenture, he blends applied research (ICLR and NeurIPS publications with 400+ citations) with hands-on engineering in Python, SQL, Rust and Spark. He’s experienced across the modern data stack (Airflow, dbt, Snowflake, Databricks) and has driven production models for credit, anti-fraud, user acquisition and support. As a manager and mentor he’s promoted senior ICs to staff level and led teams across geographies, while contributing to open-source tooling like AmpliGraph to improve code quality and dataset usability. A Stamp 4 holder in Ireland, he combines startup grit with enterprise discipline to deliver measurable business impact.
11 years of coding experience
11 years of employment as a software developer
Master of Science (M.Sc.) Electrical Engineering, Master of Science (M.Sc.) Electrical Engineering at Universidade Estadual de CampinasBachelor's degree Computer Science, Bachelor's degree Computer Science at Bishop's University
Portuguese, English, Spanish
Stackoverflow
291reputation
10kreached
3answers
3questions
Github Skills (15)
python10
back-end-development10
machine-learning9
knowledge-graph9
version-control9
git9
representation-learning9
pytorch6
bayesian6
zooming6
pymc6
anaconda6
javascript6
user-interface6
linear-regression6
Programming languages (5)
Github contributions (5)
Accenture/AmpliGraph
Jun 2019 - Nov 2019
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
Role in this project:
Back-end Developer
Back-end DeveloperContributions:156 commits, 25 PRs, 71 pushes in 5 months
Contributions summary:Pedro's commits primarily focused on fixing code style issues and integrating code quality tools within the project. The commit messages indicate efforts to address PEP8 violations by fixing issues across multiple files and integrating flake8 into the build script. They also merged and resolved conflicts across development branches, demonstrating active involvement in the project's codebase management and ensuring code consistency. Moreover, the user made specific changes to the dataset loading functions, likely to improve the flexibility and usability of the library.
python-libraryrepresentationpythonknowledgerepresentation-learning
learningtitans/bayesian_uncertainty
Jan 2018 - Sep 2018
Contributions:35 commits, 1 push in 8 months
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