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Camila Alves Dias

@camilaalvesdias

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Machine Learning Engineer driving production-scale deep learning for digital health, fraud detection, and AI R&D.

Brazil

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What I'm looking for

I’m looking for an AI/ML R&D role where I can ship end-to-end models to production—blending deep learning, robust MLOps, and cross-functional collaboration to deliver measurable business impact.

I’m a Machine Learning Engineer with 6+ years of experience designing, training, and deploying deep learning systems at scale across digital health, fraud detection, and computer vision. I bring a full ML lifecycle mindset—feature engineering, CNN/RNN/Transformers model design, deployment, and production monitoring.

At Samsung Brasil, I’m currently driving AI R&D for digital wellness, architecting sleep-stage classification models from large-scale physiological signals and building end-to-end feature engineering pipelines to improve robustness across diverse user profiles. I translate state-of-the-art biosignal research into practical architecture iterations that move accuracy forward, and I collaborate closely to integrate models into health platforms.

Previously, I delivered measurable impact at Globo and ClearSale by building ML-driven fraud detection systems and scalable MLOps pipelines, including BigQuery ETL for near-real-time analytics and CI/CD pipelines that reduced deployment cycles from days to hours. I’m also a PhD researcher in medical AI (dermatology + NLP), blending award-winning academic depth with industry execution.

Experience

Work history, roles, and key accomplishments

Current

AI R&D Specialist

Current

Samsung Brasil

Oct 2025 - Present (8 months)

Architected CNN/RNN models for multi-class sleep stage classification using large-scale physiological signals (EEG, heart rate, accelerometer). Built end-to-end feature engineering pipelines and collaborated with cross-functional teams to integrate models into Samsung Health platforms.

Sleep Analysis CNN RNN Data Integration

Data Scientist

Globo

Mar 2024 - Sep 2025 (1 year 6 months)

Developed and deployed ML models for real-time fraud detection, improving detection coverage while reducing false positives for a platform with ~180M users. Built BigQuery ETL pipelines for near-real-time analytics, and engineered OpenAI GPT-4/Gemini chatbots on Salesforce to reduce manual customer service handling.

Fraud Detection ETL Gemini API Integration

Machine Learning Engineer

ClearSale

Dec 2022 - Oct 2023 (10 months)

Engineered scalable PySpark pipelines for high-volume transaction processing, enabling real-time fraud scoring with reduced data latency. Migrated workflows to Databricks Feature Store, implemented CI/CD with Databricks and Azure to cut deployment cycles from days to hours, and established Azure model governance for 100% compliance and auditability.

Pyspark Real Time Fraud CI CD Pipelines Docker Data Latency Optimization

Computer Vision Engineer

PixForce

Apr 2022 - Nov 2022 (7 months)

Developed CNN-based object detection models for real-time image/video analysis and deployed OCR and facial recognition pipelines in production. Applied preprocessing and statistical evaluation to iteratively reduce inference error rates across production systems.

Computer Vision Object Detection Real Time Image Video Analysis OCR Pipelines Facial Recognition data preprocessing Model Evaluation Inference Error Reduction

Backend Developer

Compass UOL

Mar 2019 - Aug 2019 (5 months)

Built BI dashboards using PowerBI and Oracle SQL, and managed SQL databases integrated with the Google Chat API for chatbot data management in Azure. Supported analytics and chatbot data workflows for operational reporting needs.

Backend Development Power BI Oracle SQL SQL Database Management Google Chat API Integration Azure BI Reporting Data Pipelines

Education

Degrees, certifications, and relevant coursework

Unisinos

Doctor of Philosophy (PhD), Applied Computing

Activities and societies: 1st Place Best Paper Award — SBC

PhD in Applied Computing (in progress, expected 2026) researching NLP and computer vision for dermatology datasets in medical AI.

UFRGS

Master of Science (M.Sc.), Computer Engineering

2017 - 2019

Activities and societies: Academic exchange: University of Navarra, Spain. 2nd Place Best Paper — NAFIPS 2018; Outstanding Student Paper Award — NAFIPS 2019.

M.Sc. in Computer Engineering (2017–2019) with a thesis on fuzzy logic for neural network enhancement in computer vision.