Fernando Ferrer

A custom online tool that tracked how each physician in the US was doing in their field in comparison to their peers.
The tool tracked:
• Number of procedures performed
• Amount of billables
• Published articles
• Shared patients
• Social media feeds
• Conference speakers
The goal of the tool was to show to clients which physician was an influencer in order for them to pitch their products to them.

The high-performance division of the US Olympic Committee asked my team and me to develop an internal tool to collect and analyze athletes' performance data and predict possible injuries.

We collected wearable/sensor data plus questionnaire data and medical records and combined them into a data warehouse.
The orchestration was done using Airflow, and the warehouse was built on Redshift.

I developed a high-frequency trading algorithm for a boutique financial firm in Toronto. The algorithm would look at current pricing and trends of different currency pairs and would execute trades based on a predictive model.

Conducting a randomized control trial across multiple centers in multiple geographical areas presents a unique set of challenges. Traditionally sealed envelopes are used to randomize the next patient. When multiple centers are involved, that strategy can't guarantee a balanced set of arms.
That's where the central randomization system comes in.

This online tool guarantees patients are enrolled in a blind, double-blind, or balanced arm study from anywhere in the world. It provides different access levels to allow for patient unblinding in case of a serious adverse event while maintaining the blindness of the study.

Here are the Google Qwiklabs badges and quests that I've obtained thus far.
• BigQuery Basics for Data Analysts (Earned Jun 15, 2020)
• Baseline: Infrastructure (Earned May 15, 2020)
• Google Cloud Essentials (Earned May 13, 2020)