Isabel Van Zijl
About Isabel Van Zijl
Isabel Van Zijl is a Data Analytics Manager at Kinship in Los Angeles, California, where she has worked since 2022. She has a strong background in data science and analytics, with expertise in programming languages and machine learning techniques.
Work at Kinship
Isabel Van Zijl has been employed at Kinship as a Data Analytics Manager since 2022. In this role, she focuses on managing and analyzing large datasets, employing cloud-based data warehousing solutions such as Amazon Redshift. Prior to her current position, she served as Lead Data Analyst at Kinship from 2020 to 2022, where she developed her skills in data analysis and visualization.
Education and Expertise
Isabel Van Zijl holds a Bachelor of Science (B.S.) in Industrial Engineering with a minor in Dance from Columbia University, where she studied from 2013 to 2015. She also earned a Bachelor of Arts (B.A.) in Management Engineering and Economics from Scripps College, studying from 2010 to 2015. Additionally, she completed a Data Science/Data Analytics program at Springboard from 2017 to 2018. Her expertise includes programming languages such as Python, SQL, and Java, as well as advanced machine learning techniques.
Professional Background
Isabel Van Zijl has a diverse professional background in data analytics and science. She began her career as a Business Intelligence Intern at SI Group in 2014. After that, she worked as a Data Analyst at Chef'd in 2018, followed by a Senior Data Analyst position at SnackNation from 2019 to 2020. She also held a role as an Analyst at Duff & Phelps from 2015 to 2017. Her experience spans various companies and roles, contributing to her comprehensive understanding of data analytics.
Technical Skills and Tools
Isabel Van Zijl is proficient in a range of programming languages and data analysis tools. She utilizes Python, SQL, and Java for data manipulation and analysis, leveraging libraries such as pandas, numpy, scipy, and scikit-learn. Isabel is also skilled in data visualization tools including Looker, Tableau, and Google BigQuery. Her work involves employing advanced machine learning techniques, such as regression, decision trees, and clustering, to derive insights from data.