Jundiaí SP, Brazil
Phone: xxx-xxx-xxxx
Email: xxx@xxxx.xxx
Looking For: Data Scientist, Data Engineer
Occupation: IT and Math
Degree: Bachelor's Degree
Career Level: Experienced
Languages: English, Portuguese
Highlights:I've dedicated the last eight year of my life working on Business Intelligence solutions, with the more variated tools for store data, ETL process, and data visualizations. In the last two years, I've increased my data science skills and realized the potential and alternatives that it can bring to the business
Skills:python, Machine Learning, R, SQL, Spark, Business Intelligence
Goal:As the next step in my career, I'm expecting to find a challenge to develop my skills in data science, creating data solutions that can bring competitive advantages to company and leverage all data internal and external to bring insights to the business.
Certification:Nanodegree Machine learning engineering BigML summer school in Machine Learning 2016 Big data (data science) - BTechspecialization Bachelor of Technology - BTech
Honor:78º Place on Power Laws: Detecting Anomalies in Usage - Data science competition 37º Place on DriveData.org - Pover -T-Tests: Predicting Poverty - data science competition 4º Place on Women's Health Risk Assessment machine learning - Data science competition Top 8% on Kaggle - Predicting Red Hat Business Value - Data science competition
Business Intelligence Specialist 05/2015 - current
Travelers Brasil, São Paulo, SP Brazil
Industry: Insurance
Working as a business Intelligence leader of Travelers Brasil.
Developing Business Intelligence projects, such as dashboards, data analysis and predictive models for the insurance industry.
That involves manage 3 people, 9 dashboards (Qlikview/VBA) and 2 predictive models built.
That position increases my knowledge on Brazilian P&C and Bond Insurance market and ability to work in a small team.--
Udacity 04/2017 - 05/2018
São Paulo, SP, Brazil
Degree: Professional Degree
Major:Nanodegree
Machine Learning nano degree program, where I've learned the fundamental methods of model assessment and validation, supervised learning, unsupervised learning, reinforcement learning, and deep learning.
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