Luke Heeringa

Applied Data Scientist Ii @ Civis Analytics

About Luke Heeringa

Luke Heeringa is an Applied Data Scientist II with a background in mathematics and economics. He has experience in developing machine learning models and data pipelines, and has worked with various organizations in the field of data analytics.

Current Role at Civis Analytics

Luke Heeringa currently serves as an Applied Data Scientist II at Civis Analytics, a position he has held since 2023. In this role, he focuses on developing machine learning models and interpreting results for diverse audiences. His work involves collaborating with various organizations to provide data-driven insights and solutions.

Previous Experience at Civis Analytics

Before his current position, Luke worked as an Applied Data Scientist I at Civis Analytics from 2021 to 2022. During this time, he contributed to the development of production-level code for data pipelines and utilized both supervised and unsupervised learning methods to create machine learning models.

Education and Expertise

Luke Heeringa studied at Northeastern University, where he earned a Bachelor’s Degree in Mathematics and Economics from 2014 to 2018. He further enhanced his skills by completing a Data Science Immersive program at General Assembly from 2020 to 2021. His education has equipped him with a strong foundation in data science and analytics.

Background in Community Development and Health Education

Prior to his data science career, Luke worked as a Planning Assistant at Action for Boston Community Development, Inc. from 2019 to 2020. He also served as a Health Educator at Peer Health Exchange from 2014 to 2015. These roles provided him with experience in community engagement and program planning.

Technical Skills and Projects

Luke has developed expertise in scoping, planning, and implementing data pipelines primarily using Python and SQL. He has collaborated with commercial organizations to tackle media measurement challenges through Big Data Analytics solutions. His ability to communicate complex machine learning results to both technical and non-technical audiences is a key aspect of his work.

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