Everaldo Aguiar Email & Phone Number

San Francisco, Ca, Us

I lead the Data Science Core and Machine Learning Engineering teams at PagerDuty.Our teams are responsible for the research and deployment of Machine Learning and GenerativeAI features that are embedded in multiple PD products, helping customers with automation, noise reduction, visibility and more!

Boston, Ma, Us

Teaching elective Data Science courses as part of the Master of Science in Computer Science curriculum. My courses cover a variety of data mining and machine learning topics, also exposing students to a wide range of Python packages that allow them to quickly create and evaluate predictive models and tools that allow these models to be productionalized.

Ottawa, On, Ca

Led the Organic Growth Data Science team, providing insight and support to a number of missions focused on topics such as SEO, experimentation, incentives, and growth loops.

Bellevue, Wa, Us

Led a team of Data Scientists and Engineers responsible for a large array of Machine Learning services used across the business to automate various SAP Concur customer-facing features.Among other projects, my team supported:☑ A Kubernetes/microservices based deployment pipeline used to operationalize our 40+ services across various production environments☑ ML services and models embedded into Concur Mobile and our Expense, Invoice and Travel products☑ An array of instrumentations that automatically monitored and evaluated each one of production services

Bellevue, Wa, Us

ExpenseIt Pro from SAP Concur automatically turns receipt images captured using mobile devices into expenses, making it extremely easy for travelers to manage their travel and expenses on the go. I worked as part of a team of data scientists that developed, deployed, maintained and evaluated the machine learning models that afford that functionality. A subset of my contributions were:☑ Developed automated pipelines to build/test/deploy container-based micro-services that house our machine learning models and run on top of a large and scalable Kubernetes cluster.☑ Collaborated with data scientists, product owners, engineers and developers to incorporate machine learning solutions to their products and services.☑ Created terabyte-scale solutions to both train and evaluate machine learning models that are used in production with no downtime.

Bellevue, Wa, Us

Trained, deployed and evaluated machine learning APIs for both internal and customer-facing applications. Some of my projects allowed me to learn more about:- The full scientific computing Python stack (scikit-learn, pandas, numpy, etc)- Couchbase: NoSQL Database- Kubernetes- Neural networks (Tensorflow, Keras, Theano)- Jenkins automation- Data ETL using Spark, Pig, Hive- Natural language processing- Ensemble methods

Bellevue, Wa, Us

Developing intelligent Machine Learning models that are capable of extracting relevant information from highly unstructured data.Created automated pipelines that can train predictive models using data that is stored in Hadoop and may contain millions of instances. Assessed model performance using a wide range of contextualized metrics, and deployed stand-alone APIs using technologies such as Docker, Mesos and Marathon.

Notre Dame, In, Us

As a PhD student at the University of Notre Dame, I worked with a large range of collaborators to tackle problems in the field of education through the informative use of student-level data, machine learning, and predictive analytics. I developed a model to detect, among first-year engineering students, who may be at risk of leaving the program. This particular model exceeded the expectations in terms of its accuracy by combining academic performance data with an engagement proxy leveraged from electronic portfolio usage. This allowed educators to not only detect students that were at risk due to lower academic achievement but also those who, despite having high performance marks, were disengaged from their academic goals. Additionally, during my time at Notre Dame I have also worked on a variety of other projects with topics ranging from recommendation systems, secure multi-party computation protocols, clickstream analysis, cloud computing, health care analytics, and others.

Chicago, Il, Us

Fellow at the Eric & Wendy Schmidt Data Science for Social Good fellowship working on the development and evaluation of models to predict student success.I joined DSSG in the summer of 2014 and began working on a very interesting project in partnership with the Montgomery County Public School system, the largest school district in Maryland with approximately 150,000 students enrolled. Our objective was to improve upon an early warning system currently being used by MCPS to detect students that are at risk of not graduating on time. Over the course of the summer fellowship, we developed machine learning models that were able to leverage knowledge from the historical student-level data available. Ultimately, our models were able to significantly improve the accuracy of our partner's EWI system as well as provide a much wider range of information that can now be used to help students graduate. Since then, we have continued working on this project and are now expanding to other large and medium-sized school districts in the US. Portions of this work have been presented at KDD, while others have been accepted for publication at a top Learning Analytics conference.

Phd, Computer Science And Engineering

Master Of Science (M.Sc.), Computer Science And Engineering

Bs - Summa Cum Laude, Computer Science / Mathematics