David O. Profile

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$20 USD / hour

palma,

spain

$20 USD / hour

It's currently 1:16 PM here

Joined May 24, 2019

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David O.

@davidolmoperez

0.0

(0 reviews)

0.0

$20 USD / hour

palma,

spain

$20 USD / hour

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Python developer, data engineer, data scientist

Python professional that has extensive experience in end-to-end projects (from definition architecture to production deployment). I have experience in all the parts of projects that involve data: Serverless Architecture, data orchestration, ETL, data validation, machine&deep learning, front-end, visualization, GIS I have worked with multiple cloud technologies. My experience can be summarized as Python Developer, Data Engineer and Data Scientist. I have experience as well in front and backend with Vue.Js, Flask and Dash-Plotly, and Firebase Keywords: Python, Google Cloud, Software Deployment, Vue.Js, Machine Learning, Deep Learning, Amazon Web Services, Folium, Pandas, Scikit-Learn, XGBoost, LightGBM, CatBoost, Plotly, Dash, R, Ggplot2, Plotnine, Javascript, Ortools, Mathematical and Discrete Optimization, CSS, HTML, Git, Constraint programming, Leaflet, Firebase, Web Scrapping, Apache Airflow, Tableau, Power BI, API, Heroku, Big Query, Serverless, Google Cloud Functions, GIS

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Portfolio

Use of Apache Airflow to orchestrate a data processing complex pipeline. It orchestrates Amazon Lambda functions and Google Cloud Functions

Apache Airflow and Cloud Functions/Lambda data pipeline

Prediction system for harvest of big agriculture companies. As variables, it uses massive meteorological data (from company meteo stations, meteo data banks and public stations), soil data, terrain and geographic data, historical production data and specific plant type data. The prediction system uses the stacking technique of the following algorithms: Gradient Boosting (XGBoost, Catboost, LightGBM), linear models (ElasticNet or Ridge/Lasso Regression), K-Nearest-Neighbors. It is presented in an interactive chart system based on Dash - Potly

Harvest volume prediction

Web App that uses meteorological Big Data to predict specific meteorological conditions and pest&diseases for agriculture land. Behind the scenes uses machine learning (XGBoost and Scikit-Learn), clusters and spatial analysis. It is visualized in a geographic system (Leaflet/Folium) with interactive charts with Dash-Plotly. Included interactive legend and chat for company-customer interaction.

Web app with machine learning prediction

Apache Airflow and Cloud Functions/Lambda data pipeline

Harvest volume prediction

Web app with machine learning prediction

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Experience

Freelance

Oct 2017 - Present

End-to-end data science for multiple projects (from architecture definition to production deployment) Python professional that has extensive experience in end-to-end projects (from definition architecture to production deployment). I have experience in all the parts of projects that involve data: Serverless Architecture, data orchestration, ETL, data validation, machine&deep learning, front-end, visualization, GIS

Grifols

Apr 2015 - Apr 2018 (3 years)

-Data Science and machine learning deployment -A/B Testing -Management of complex data pipelines with non-structured data -Gathering, cleaning, exploring, combining and linking Data from different systems and sources -Global data projects -SAP Success Factors database reporting administrator