Spanish version here

Freelance computer engineer, with 10+ years of experience. Specialized in data projects: data development in Python and R and cloud infrastructure. Working as an individual contributor or with my team (skilled freelance data scientists and web devs).


☎️ Contact

πŸ“§ Email: luzfrias at circiter dot es

🐦 Twitter: koldLight

πŸ™πŸ± Github: koldLight

πŸ”— LinkedIn: Luz Frias

πŸ”— Company website: Circiter

πŸ‘©πŸ»β€πŸ’» Projects

Mortality Monitoring (MOMO)

Instituto de Salud Carlos III (ISCIII) - Spanish public health organization

Automation of the process of mortality data ingestion from civil registries, processing, modeling and generating reports and alerts from MOMO. The objectives were to alert and measure excesses and attribute mortality to events such as heat waves or flu.

MOMO received a lot of attention during the Coronavirus health crisis and it was a key project to estimate the real impact of the epidemic on Spanish mortality.

πŸ”— MOMO

πŸ”— Kairos

During the health crisis, I also developed the ISCIII data publication and monitoring panel, and the calculation of the reproducibility index by region, which was reported to other organizations.

πŸ”— COVID-19 Dashboard

Main technologies: R, Python, Docker, Flask, Flexdashboard.

Modeling techniques: GAM, mixed-effects models, survival analysis.

Sentinel surveillance system on respiratory infections

Instituto de Salud Carlos III (ISCIII) - Spanish public health organization

Design and build the new surveillance system for the endemic phase of the COVID-19, and the rest of existing respiratory infections such as the influenza virus. The main objective of this application is to unify the data provided from the different autonomous communities of Spain and run automatic rules and validations to ensure data integrity. This system also enables the administrators from changing the required data without code upgrades, so it adapts quickly to regulation changes.

πŸ”— System information (spanish only)

Main technologies: Python, Docker, Flask, MySQL.

Marketing analytics and optimization dashboard

Roya - USA marketing startup

Create a demo platform for the product. The client was a NY based startup with a strong analytical product for optimizing marketing processes in big companies. This included:

Main technologies: R, Shiny, Shiny Server, flexdashboard, Python, Flask, Linux, nginx.

Preventive maintenance on wind generators

Atlantica - Renewable energy

The main objective was the development of a heating alarm system on the electric transformers installed in multiple wind plants in South-America. The project included a detailed Exploratory Data Analysis, data inconsistency detection, and the heating model. The biggest challenge was the feature engineering, to take into account the physical rules and the heat behavior over time.

Main technologies: Python, Jupyter Notebooks.

Modeling techniques: GAM.

Transit analysis in physical stores

Johnson Controls - Buildings infrastructure and IoT

Development of a system to analyze people transit in physical spaces through cameras. My main objectives were:

Main technologies: R Shiny, Python Flask, Bootstrap, Google Cloud (Compute Engine, Cloud Functions and BigQuery).

Modeling techniques: trigonometry, minimization of error functions.

Data Warehouse and dashboard for e-scooter sharing service

Acciona Motosharing - Mobility

Design, develop and deploy the Data Warehouse to analyze the e-scooter sharing quality service, follow the vehicles incidences, the customers behavior and take strategic decisions. I led the tech team.

The goals we achieved:

Main technologies: Python, Flask, Dash, PostgreSQL, GCP.

Interaction prediction in email marketing

MDirector - Email marketing platform

Development of a prediction system for openings and clicks in an email marketing system.

The objectives:

Main technologies: Python, Docker, AWS, MySQL

Modeling techniques: random forests, linear optimization.

Google Ads performance tuning

OrbitalAds - SEM optimization startup

Creation of machine learning processes to increase the performance of Google Ads campaigns. Specialization in:

Main technologies: Python, Docker, Google Cloud (Compute Engine, Cloud Build, BigQuery, GKE, datastore).

Modeling techniques: TFIDF, cosine distance and other NLP and other common techniques in NLP.

IoT systems management application

Johnson Controls - IoT for big corporations

Development of a management application for large IoT systems in buildings to measure supplies use, temperature, air quality and others. Integrated in Metasys, a world’s leading building automation system.

πŸ”— Metasys

Main technologies: Python, Flask, React.js, PostgreSQL.

Industry production planning

Reny Picot - Dairy industry

The objective was to propose quantities to be produced per product to minimize losses due to stock breakage or expiration. We develop a demand forecasting model, a stock evolution simulator and a web panel to view and download the results.

Main technologies: R, Shiny.

Modeling techniques: simulation and optimization techniques.

Renewable energy production modeling

Kren4 - Renewable-energy power plants management platform

Modeling of production in wind and solar generators based on sensor readings to detect machine failures. Integration with the customer IoT system and report automation. Development of an API to order actions and view system results.

Main technologies: ElasticSearch, Python, AWS, MySQL.

Modeling techniques: random forests.

R and Python for data analysis teaching

EAE Business School, EDIX, Bank of Spain, Acciona and other companies

Associate professor of the Big Data master at EAE. I also usually teach programming training in R and Python oriented to data analysis in other companies.

I've openly publish some of my courses (in spanish):

πŸ”— Data visualization with R

πŸ”— Data analysis with Python

πŸ”— Data analysis with R

πŸ”— Data visualization introduction with D3.js


πŸ›  Tech skills

This is the tool set I'm used to work with:

SQL

Complex queries and optimization of these in relational databases (MySQL, Oracle, Teradata, ...). PostgreSQL at a deeper level, such as optimization of index types, DB parameters, or use of PostGIS.

Sporadic use of non-relational databases like Mongo.

Data analysis and modeling

Python (pandas, scikit-learn, numpy, ...) and R.