Tiago Dos Santos
About Tiago Dos Santos
Tiago Dos Santos is a Machine Learning Engineer currently working at Cleo in Lisbon, Portugal. He has extensive experience in data science and machine learning, having previously worked at companies such as NannyML, DefinedCrowd Corp., and Feedzai.
Current Position as Machine Learning Engineer at Cleo
Tiago Dos Santos is currently employed as a Machine Learning Engineer at Cleo in Lisbon, Portugal. In this role, he works on developing and deploying machine learning models to support Cleo's financial services. His responsibilities include optimizing data pipeline operations and ensuring the reliable performance of machine learning systems in production.
Previous Role as Senior ML Engineer at NannyML
From 2021 to 2022, Tiago Dos Santos worked as a Senior ML Engineer at NannyML for a six-month tenure. During his time at NannyML, he focused on building and refining machine learning models and collaborating with cross-functional teams to integrate these models into the company's services.
Experience with Machine Learning and Data Infrastructure at DefinedCrowd Corp.
Tiago Dos Santos held multiple positions at DefinedCrowd Corp. between 2019 and 2021. Initially, he served as a Machine Learning Engineer for two consecutive periods. He also worked as a Data Infrastructure Engineer, contributing to the development and maintenance of core data systems. During his tenure, he focused on improving data workflows and implementing scalable data solutions.
Academic Background at Universidade Nova de Lisboa
Tiago Dos Santos studied at Universidade Nova de Lisboa, where he completed a Bachelor of Science (BS) in Computer Science from 2009 to 2013. He then went on to achieve a Master of Science (MS) in Computer Science from 2014 to 2017. Additionally, he earned a Post-Graduate degree in Analysis and Engineering of Big Data. His academic training provided a strong foundation for his expertise in machine learning and data science.
Technical Skills and Tools Utilized
Tiago Dos Santos has extensive experience with several tools and technologies used for data engineering and machine learning. He uses Airflow for orchestrating workflows, FastAPI for deploying machine learning models, and Dask for parallel computing tasks. He leverages Kafka for log management, Superset and Drill for creating dashboards, and Cassandra and PostgreSQL for managing databases. He also employs Parquet and Drill for OLAP analysis, Druid and Presto for model insights, and HDFS for data partitioning. Additionally, he uses Azumarill for troubleshooting production issues.