Paulo Arantes
ETL EngineerLandscape Horticulture, Data Science & Business
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Landscape Horticulture, Data Science & Business
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I am passionate about climate and community resilience through food sovereignty and landscape designs that regenerate the soil. I enjoy public speaking and conveying complex concepts and emotions through storytelling. I have a background in business and data science and over the past 16 years I’ve helped entrepreneurs grow and scale their businesses by leveraging data to build products and make strategic decisions.
Udacity
Machine Learning Engineer Nanodegree, Machine Learning
January 1, 2019 – January 1, 2019
Udacity
Artificial Intelligence Nanodegree, AI & Deep Learning
January 1, 2017 – January 1, 2017
International Business School - FGV
Bachelor of Business Administration (BSBA), Business Administration, Management and Operations
January 1, 2010 – January 1, 2013
Homestead Design Collective
Edible Landscape Gardener
July 1, 2025 – Present
Merritt College
Landscape Horticulture Student
November 1, 2023 – Present
Oakland, California, United States · Hybrid
SPAN
Senior Data Scientist
May 1, 2022 – July 1, 2024
Climatebase
Climate Fellow
January 1, 2022 – May 1, 2022
Autodesk
Senior Data Scientist & AI Ethics Ops
February 1, 2019 – May 1, 2022
San Francisco, California, United States
BuildingConnected
Machine Learning Engineer
August 1, 2018 – January 1, 2019
San Francisco Bay Area
UnifyID
Machine Learning Engineer
December 1, 2016 – August 1, 2018
San Francisco
Galvanize
Data Science Fellow
April 1, 2016 – November 1, 2016
San Francisco Bay Area
Mitoo (company shutdown)
Data and Operations
July 1, 2015 – April 1, 2016
San Francisco Bay Area
Denox (refocused ops only to Brazil)
USA Operations / Business Development
September 1, 2014 – June 1, 2015
San Francisco Bay Area
Endeavor Brasil
Search & Selection of High Impact Entrepreneurs and Growth Support
April 1, 2012 – August 1, 2014
Minas Gerais
Lance da Vez
Commercial Manager
December 1, 2010 – December 1, 2011
Greater Belo Horizonte
Sebrae-MG
Agribusiness Department Intern
June 1, 2009 – December 1, 2009
Belo Horizonte
Capstone: Predicting Art Purchase and User Engagement for Vangoart.co
July 1, 2016 – Present
Vango is an art marketplace where you can get original art from independent artists. I scoped some challenges to focus on with the leadership: - What’s the best timing to engage users? - Can we predict such an abstract purchase? - What are purchase habits like? - How much money can we make? Pipeline process: 1) Aggregate event based data from Mixpanel (12GB, 96 events tracking 141 different features) with user based data from Postgres Database (3GB, 104 tables) 2) Data Exploration and Processing 3) Feature Engineering trying different engagement metrics 4) Apply SMOTE to treat imbalance class issue 5) Logistic Regression to extract log odds impact on purchase 6) GridSearch Random Forest models to Predict probabilities of purchase per user 7) Setup automatic script to run every 3 months (time based on EDA discoveries of purchase cycles) Findings: What’s the best timing? Now. Can we predict such an abstract purchase? Yes. What are purchase habits like? Takes 103 days for first purchase, repeats every 108 days How much do they spend? About $265 How much money can we make? Conservatively, +14.3% revenue -> With the script, it is now possible for them to target users with best chances of making a purchase, and re-run the predictions at any time without retraining the entire model.
Let Me Stand NEXT To Your Fire
June 1, 2016 – Present
We prepared and analyzed data from all 911 calls in Seattle from 2011 to 2016 and created an animated visualization of reported fires using geographic coordinates and comparing their locations to the Fire Department units across the city. Curiously, the FD stations are located near all 11 aircraft crashes that occurred in this time frame (you can change the filters on the link), and to specifically well distributed comparing to the fires caused by food left on stove top. Considering all possible fires, there are some locations that could be improved, so we created a program that calculates the euclidean distance from each fire and assigns the closest Fire Station (not available on the visualization).
Habits of the SF Bay Area Bike Share cyclist
April 1, 2016 – Present
Exploratory Data Analysis on SF Bay Area Bike Share data using Python, Numpy, Pandas, and Seaborn that involved: 1/ Creating hypothesis to determine the variables at the origin of an observed behavior. 2/ Identifying real data bases that we are going to use to test our model. 3/ Merging these different data bases to extract information on correlated variables gathered by different sources. 4/ Predicting future behavior.
Grupo Oilcheck - Recruitment and Selection as Endeavor Entrepreneur
December 1, 2012 – Present
For Luis Milani and Carlos Alves, oil analysis is to a machine what a blood test is to the human body – a diagnostic for impending system breakdowns. Oilcheck offers heavy machinery owners solutions for 1) predictive maintenance through the precise analysis of machinery fluids and 2) preventive maintenance via oil filtration products. The global heavy machinery industry is vulnerable to unexpected downtime and high maintenance costs. In Brazil, only 10% of machines in the equipment-dependent sectors of construction, mining and agriculture currently undergo oil analysis; however, this 10% represents a potential market for Oilcheck of US$900 million in 2012, and nearly US$1.8 billion in 2015. With just 45 employees, Oilcheck is already the second largest fluid analysis laboratory in Brazil, processing 120,000 samples per year. By employing an efficient, independent, and customer-oriented solution, the entrepreneurs aim to become global industry leaders. - See more at: http://www.endeavor.org/entrepreneurs/carlos-henrique-alves/1912#sthash.Xkk0l7JV.dpuf
Plano de Negócios NewPacking do Brasil
May 1, 2011 – June 1, 2011
Plano de Negócios para uma empresa de importação de máquinas de reciclagem de isopor
Machine Learning by Stanford University (Andrew Ng)
Coursera Course Certificates
June 24, 2026 – Present
Regenerative Landscape Design Course (20 weeks)
Balkan Ecology Project
June 24, 2026 – Present
Permaculture Design Certification (PDC)
Midwest Permaculture
June 24, 2026 – Present
Neural Networks for Machine Learning by University of Toronto (Geoffrey Hinton)
Coursera Course Certificates
June 24, 2026 – Present
Certified Practitioner of Human-Centered Design
LUMA Institute
June 24, 2026 – Present
Deep Learning by Google
Udacity
June 24, 2026 – Present
Communicating with Empathy
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a significant pivot from data science/ML roles to landscape horticulture, which is a notable deviation from the target role of ETL Engineer. While there's a strong foundation in data, the recent focus on non-technical fields might indicate a shift in career interests or a lack of recent hands-on experience in core ETL technologies. The projects demonstrate a strong analytical and problem-solving approach, but the diversity of roles (including non-technical ones) might suggest a broader interest set that may not align perfectly with a focused ETL engineering role. The 'Climate Fellow' and 'Senior Data Scientist' at SPAN roles show an interest in climate tech, which could be a cultural fit if the ETL role is within a similar domain.
Soft Skills & Operational Fit
The candidate's experience includes leading cross-functional teams, managing projects, and improving operational efficiency (e.g., reducing request times, optimizing data processing). Participation in ERGs and external panels suggests strong communication and collaboration skills. The diverse project background indicates adaptability and a problem-solving mindset.