• Built data pipeline to generate aggregated features and training data using Scala/Spark; supporting the machine learning model's real-time likelihood score computation.

• Computed and accessed aggregated features in Google Cloud Platform.

• Added several important relevant features to the spam machine learning model; improved spam detection accuracy by approximately 1.5%.

• Built analytics search engine using Elasticsearch to allow for faster, more scalable user searches.

• Handled importing of data from Hadoop HBase and performed transformations using Hive.

• Developed Java MapReduce program to support distributed data processing on a large dataset (36 million records).

• Developed a data migration environment for a bitcoin settlement system using Python Flask, optimizing SQL queries to achieve automated data migration and increase efficiency within a blockchain solutions company.

• Provided critical services to business units in Singapore and New York City, demonstrating adaptability and effective communication in a fast-paced crypto-focused environment.