Zach Blumenfeld Picture

Zach Blumenfeld

Data Science Product Specialist, Neo4j

Zach Blumenfeld is a graph enthusiast who helps data scientists, engineers, and business leaders understand and implement Graph Analytics to solve challenging business problems.

He has firsthand experience with a wide range of modern day analytical challenges, including criminal fraud detection, identity resolution, and recommendation systems. Serving in both data science and software developer capacities, Zach has applied graph computing for law enforcement and government entities in support of missions that counter drug trafficking, human smuggling, money laundering, and child exploitation. He has led the development and deployment of full stack graph systems designed to facilitate broad search and analytical query requirements.

Zach is excited to join Neo4j as Data Science Product Specialist, where he will help empower the field with Neo4j’s industry leading Graph Data Science (GDS) capabilities.

Latest Posts by Zach Blumenfeld

The Definitive Guide to Building a Predictive Model in Python

Predictive modeling is one of the most fundamental tasks of a data scientist, and you’ll encounter it in nearly every job and industry in the field. Keeping your knowledge and skills up-to-date is essential to driving efficiencies and revenue at your company. In this article, you'll... read more

Predictive Modeling Techniques: Types, Benefits & Algorithms

With the immense amount of data being generated daily, organizations are drawn to advanced analytics, data science, machine learning, and AI to drive better forecasting, more accurate predictions, and truly novel innovations. But many businesses fail to reap these benefits. Instead, they... read more

Demystifying Graph Neural Networks

Graph Neural Networks (GNNs) are gaining tons of recognition in the machine learning community due to their potential for solving complex tasks in social networks, drug discovery, recommendation systems, and more. Unlike traditional neural networks that operate on fixed-size, ordered... read more

Exploring Practical Recommendation Systems In Neo4j

In this post we explore how to get started with practical and scalable recommendation in graph. We will walk through a fundamental example with news recommendation on a dataset containing 17.5 million click events and around 750K users. We will leverage Neo4j and the Graph Data Science (GDS)... read more

Exploring Supervised Entity Resolution in Neo4j

Photo by Alina Grubnyak on UnsplashWhile Supervised Entity Resolution (ER) can be immensely valuable, it is sometimes difficult to apply and scale in the real-world enterprise setting.In this post, I explore how the Neo4j Graph Data Science (GDS) library can be applied to rapidly develop... read more