I'm Vinícius Nunes da Costa, a Data Scientist and ML Engineer based in Recife, Brazil. I build applied AI systems where machine learning engineering, clinical research, and business impact meet.
What I do
Applied AI from research to production
My work covers the full arc from research to deployment. On the technical side, that means designing and shipping ML pipelines, fine-tuning language models, building computer vision systems, and keeping production services reliable with MLOps.
I focus on domains where the stakes are real: healthcare, logistics, and data-intensive business operations. Over four years across startups and consulting, I’ve learned that the hardest and most valuable part of applied AI is turning a working prototype into something a team can maintain, trust, and improve.
Background
Formal training and context
I hold a B.Sc. in Computer Science from CESAR School (2020–2023) and a B.A. in Business Administration from FCAP–UPE (2020–2025). I chose the dual-degree path deliberately: technology alone rarely creates durable value without understanding the organizational and economic context around it.
I’m currently completing an M.Sc. in Computer Engineering at POLI–UPE (2025–2027), where my research focuses on early anomaly detection for epidemiology time series. My experience in econometrics, computational modeling, and applied AI comes from FCAP–UPE, while at CESAR School my research centered on NLP, learn-to-rank mechanisms, semantic search, and retrieval re-ranking for automatic ICD coding. I also hold a CEFR C2 English certification (Michigan ECPE, 2023).
Computer Science
CESAR School, 2020–2023
Business Administration
FCAP–UPE, 2020–2025
M.Sc. in progress
POLI–UPE, 2025–2027
Core competencies
Technical stack
Machine Learning & AI
Supervised and unsupervised learning, deep learning, NLP and LLMs, computer vision, recommendation systems.
MLOps & Engineering
FastAPI, Docker, AWS, GCP, Azure DevOps, CI-friendly templates, Poetry, dbt.
Data & Analytics
Spark, Hadoop, ClickHouse, ETL/ELT pipelines, A/B testing, exploratory analysis.
LLM Tooling
LangChain, LangGraph, HuggingFace, Guardrails AI, Elasticsearch, RAG architectures.
Domains I care about
Where I do my best work
Healthcare AI
Clinical NLP, diagnostic support, treatment decision systems, and explainability in high-stakes settings.
MLOps & production ML
Turning AI into reliable services and systems that teams can iterate on and trust in production.
Applied research
Problems between engineering and science, where rigorous method and practical judgment both matter.
Work history
Condensed timeline
Focus Distribuidora — AI Consultant
Apr 2025 – present
Sales intelligence, data governance, churn modeling, and digital transformation in logistics.
Vitally Health — Mid-level Data Scientist
Dec 2024 – Mar 2025
Clinical decision support for hypertension and heart failure, medication triage systems, FastAPI + AWS Lambda deployment.
Pickcells — Junior Data Scientist
Mar 2024 – Nov 2024
NLP/LLM pipelines for biosignal extraction, computer vision for cancer detection, and an MLOps template (ai-dat).
Oncase — Data Science Intern
Nov 2022 – Dec 2023
Ensemble clustering, LightFM recommender systems, and Big Data pipelines with Spark.
Beyond work
What keeps me engaged
I’m curious about the theory behind what I build. I’ve been a teaching assistant for Statistics & Probability and Machine Learning at CESAR School, and I think explaining things clearly is one of the best ways to find the gaps in your own understanding.
The most interesting problems in AI are interdisciplinary by nature, so I keep one foot in engineering and one in business and research.
Get in touch: LinkedIn · GitHub · viniciusnvcosta47@gmail.com