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Meina Zhou

Hello, I am Meina Zhou. I am the Data Science Manager at Indellient. My core expertise lies in the application of proven data science tools and techniques to conduct business analytics and predictive modeling. I have used my business acumen and data science skills to solve business problems. I am a thought leader in the data science world and an active conference speaker. I enjoy public speaking and sharing innovative data science ideas with other people. I have received my Master of Science in Data Science from New York University and my Bachelor of Arts in Mathematics and Economics from Agnes Scott College.
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Data & Analytics

Calculating Dynamic Customer Lifetime Value (DCLV) using Data Science

March 3, 2022December 10, 2020

The dynamic CLV calculation takes each client’s background information, product usage, historical complaint, and billing data as input, and generates a unique predicted customer lifetime value as output...

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Data & Analytics

Creating a Data Science Team

August 29, 2022August 13, 2020

Generally, a data science team is composed of Data Scientists, Data Engineers, Machine Learning Engineers, and Data Analysts......

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Data & Analytics

How to Build a Fraud Detection System using Machine Learning Models

August 29, 2022July 8, 2020

Using Machine Learning and Data Science can help your company detect fraud and asses risk. 4 steps on how to build a Fraud Detection System with your data....

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Data & Analytics

How to Use Machine Learning to Detect Fraud

August 29, 2022May 20, 2020

Sample applications of machine learning models include prediction, image/speech recognition, recommendation engine, and fraud detection....

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Indellient is a Cloud, DevOps, and Data consulting company. We partner with our clients to provide end-to-end solutions on cloud migration, cloud cost management, continuous integration, continuous delivery, data consolidation, data management, advanced analytics and more. As a complete lifecycle partner, Indellient works across all industries and company sizes ranging from startups to large enterprise.

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