Angelo Klin

Angelo Klin has a diverse work experience in the field of data science and analytics. Angelo started their career in 2015 as a Volunteer Data Scientist at ABRISA, where they investigated census data and developed models to improve grant success rates and community services.

In 2016, Angelo joined General Assembly as an Instructor, teaching Data Science, Text Analytics, and Statistics until 2019. During this time, they also worked as a Master Data Scientist at Katra Analytics, where they used data for decision making, provided consulting services, and developed content for teaching and training.

In 2017, Angelo worked as a Senior Data Scientist at Immersive, where they explored breach indicators in procurement to identify potential fraud and developed models to flag irregular transactions.

From 2016 to 2017, they also served as a Data Scientist at Telstra, where they developed security detection data models to improve threat prevention and resolution, leading to significant savings and productivity gains.

In 2018, Angelo worked as a Data Scientist at Axicor, modeling driver's profiles to assess risk and reduce costs. Angelo also served as a Data Science Instructor and Advocate at DataRobot from 2020 to 2021, where they offered Data Science knowledge and processes to support business initiatives and taught best practices in Data Science.

Angelo's passion for education led him to become an Education Committee Member at the Data Science Institute of Australia in 2019. Angelo played a key role in developing certification standards and industry-level education.

Currently, Angelo Klin is working as a Data Scientist at Wagetap, where they develop models on credit loan repayments, performs data analysis, and ensures data quality.

Throughout their career, Angelo has utilized various technologies like Python, DataRobot, and Nuix for text analytics, cluster analysis, and data science practices. Angelo has consistently demonstrated their expertise in data science, analytics, and visualization to drive positive outcomes for organizations.

Angelo Klin has an extensive education history in the field of data science and statistics. Angelo completed a specialization in Data Science from The Johns Hopkins University in 2014. In 2015, they pursued multiple specializations and online training courses related to data science, statistics, and machine learning from various institutes, including the University of Washington, Wesleyan University, and the SAS Institute. Angelo also completed a specialization in Data Analysis and Interpretation from Wesleyan University and a specialization in Data Science at Scale from the University of Washington.

In 2016, Angelo enrolled in the University of California, Berkeley, where they studied Data Science and Engineering with Spark. During the same year, they also pursued a Methods and Statistics in Social Sciences Specialization from the University of Amsterdam.

Apart from their formal education, Angelo obtained several certifications in various subjects. Angelo earned certificates for courses such as Machine Learning Specialization, Basic Statistics, Inferential Statistics, and Quantitative Methods from Coursera. Angelo also obtained edX Verified Certificates for courses like Big Data Analysis with Apache Spark, Distributed Machine Learning with Apache Spark, and Introduction to Apache Spark.

Overall, Angelo Klin has a strong academic background in data science, statistics, and machine learning, supported by a range of certifications and specializations from reputable institutions.