Data Engineer, Advanced Analytics

• Sep 2022 - Present

CEO - Cofounder Brothers in Code

• Feb 2019 - Present

Working knowledge of AWS Services IaaS, PaaS, and SaaS and coming trends in the Cloud space.Serving as a hand‑on subject matter expert for DevOps and Automation in an AWS infrastructure environment.Solid foundation of knowledge around AWS cloud services and the cloud ecosystem.Work with business units, software developers, security, to design and build applications and services within AWS and/or other cloud providers.Provide daily monitoring, management, troubleshooting and issue resolution to systems and services hosted on cloud resources.Work as part of a team, to design and develop cloud data solutions.Manage and support full application stacks from the OS through custom applications using Amazon cloud-based computing environments.Manage a continuous integration/continuous deployment methodology for our server-based technologies.Participate in planning, implementation, and growth of our infrastructure on Amazon Web Services.Analyzing and monitor performance bottlenecks and key metrics to optimize software and system performance.Be part of the client’s scrum team and work on user stories.Design, manage, and maintain tools to automate operational processes.Provide technical direction and oversight to cloud implementation teams.Providing analytics and forecasts for cloud capacity, troubleshooting analysis, and uptime.

Data Science Auditor

• May 2021 - Sep 2022

• Development of a Dynamic Audit Model using Business Rules that allows decisions to be made about the next Audit to be executed in the ACHS, which is taken to an implementation based on Databricks on Azure.• Execution of a Machine Learning Operations consultancy MLOps (Machine Learning Operations) on the Analytics Management of the Chilean Security Association ACHS.• Teaching Auditors of the Python tool to execute their Audit analysis.• Teaching Auditors Descriptive and Predictive Analytics Using Python.• Automation of Internal Audit processes using Robotic Process Automation (RPA) and Automation Anywhere.• Execution of projects with agile methodologies Using the Azure DevOps Tools stack: Boards, Repos, Pipelines, Test Plans and Artifacts.

Postdoctoral Research Assistant

• Jan 2020 - Dec 2020

Implementation of the Iris Recognition Algorithm for identity selection.Creation of a database of iris images of subjects who are under the influence of alcohol.Implementation of eye segmenters (iris, pupil, sclera removal) based on U-Net (Neural Networks) segmenters to measure characteristics such as iris radius, pupil radius, etc. Implementation of the Iris Recognition Algorithm for identity selection.Creation of a database of iris images of subjects who are under the influence of alcohol.Implementation of eye segmenters (iris, pupil, sclera removal) based on U-Net (Neural Networks) segmenters to measure characteristics such as iris radius, pupil radius, etc.

Data Scientist

• Oct 2018 - Nov 2019

Implementation of a predictive model pipeline based on XGBoost. This pipeline was developed to predict the probability of customer renewal. This pipeline starts with a SQL query, whose output is then pre-processed to deliver relevant variables such as number of clicks, number of previous renewals, etc. These variables are then used to feed the predictive model.Implementation of a predictive pipeline for the execution of an NLP model based on artificial intelligence, which predicts the documentation tags associated with a particular text. This pipeline starts with an SQL query that delivers a text, which is then pre-processed and vectorized. This vectorization then feeds the predictive model. Additionally, an additional SQL query delivers the labels used as dependent variables.Implementation of pipeline for detection of existing documentation based on heuristics. This pipeline extracts information from texts by scrapping (texts containing tables with relevant information about companies). This information is then exported to a database via SQL. This pipeline is then stored in a docker and executed on an AWS EC2 machine.

Data Scientist

• Dec 2016 - Oct 2018

Implementation of machine learning models for the prediction of PM10 particulate material presence at the Las Pelambres mine.Development of a predictive model based on neural networks to predict the percentage of As in the calcines produced by the Minister Hales Division toaster (DMH).Determination of mineralogical and operational patterns of the DMH roaster plant and gas plant using data analysis and computational intelligence techniques.Study and analysis of mineralogical and transport models in the DMH mill plant using cluster analysis techniques.Determination of mineralogical patterns and operational patterns of the L Pelambres mining flotation plant using data analysis and computational intelligence techniques.Development of machine learning algorithms for the website www.predecire.com.

Grupo de Instrumentación Radio Astronómica

• Jul 2008 - Dec 2016

Implementation of a laboratory for the measurement of terahertz photonics based on the photomixer effect, with which the emission power of terahertz signal generation devices is measured.Design of Terahertz microwave devices using Microwave Studio CST and HFSS.Simulation of the output power of Terahertz microwave devices using Microwave Studio CST and HFSS. TCAD Simulations of the UTC-PD photodiode (Uni-Travelling Carrier Photodiode) using the physical model of Drift-Diffusion Carrier Transport Model.Measurement of output power of UTC-PD (Uni-Travelling Carrier Photodiode) photodiodes using the photomixer experiment.Development of a Finite Difference algorithm in Matlab for measuring the output power of Traveling-Wave devices.Implementation in Matlab of an analytical model based on the development of semiconductor and transmission line transport equations to determine the power of a Traveling-Wave device based on UTC-PD (Uni-Travelling Carrier Photodiode).

Grupo de Materiales Magnéticos y Nanoestructuras

• Mar 2004 - Jun 2008

Development and implementation of a MOKE (Magneto Optical Kerr Effect) system, for the magnetic characterization of thin films.Thin film growth of Mn0.6Zn0.4Fe2O4/Fe ferrites using the RF cathode sputtering technique.Characterization of Mn0.6Zn0.4Fe2O4/Fe ferrites by AFM microscopy.Magnetic characterization of ferrites of Mn0.6Zn0.4Fe2O4/Fe by the MOKE effect.Thin film growth of Ni0.5Zn0.5Fe2O4/Fe ferrites using the RF cathodic sputtering technique.Characterization of Ni0.5Zn0.5Fe2O4/Fe ferrites by AFM microscopy.Magnetic characterization of Ni0.5Zn0.5Fe2O4/Fe ferrites using MOKE effect.

Asistente Gestión del Servicio

• Oct 2001 - May 2002

Verifying of the Service Status, Customer Support, carrying out conciliations between Telecommunication Operators. Verifying of the Service Status, Customer Support, carrying out conciliations between Telecommunication Operators.