Sadrach Pierre
Senior Data Scientist at a hedge fund based in New York City
Expertise: Data science, machine learning
Education: Cornell University

Sadrach Pierre is a senior data scientist at a hedge fund based in New York City. His experience includes building out machine learning pipelines for solving a wide variety of business problems. In 2021, he published a research paper on using machine learning to identify small molecule drug targets to treat SARS-CoV-2. Pierre has built out end-to-end churn prediction systems and customer segmentation engines, and has also worked on building demand prediction and promotion optimization engines for clients in the retail space.

He has a wide range of experience applying data analysis, supervised machine learning and unsupervised machine learning to solve problems across disparate industries including finance, drug design, retail and social media. He has experience working for startups in the cryptocurrency regulation space where he built market manipulation detection engines powered by machine learning models.

Pierre has a doctorate in chemical physics from Cornell University.

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36 Articles
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How to Find Outliers (With Examples)
Outlier detection is a data science technique with applications across a variety of industries. This primer will introduce you to the basics with examples to illustrate the principles.
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A Guide to Metaheuristic Optimization for Machine Learning Models in Python
Metaheuristic optimization methods are an important part of the data science toolkit, and failing to understand them can result in significant wasted resources. This guide will help you get started.
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A Guide to Loss Functions for Deep Learning Classification in Python
Selecting the right loss function for a machine learning problem is a crucial step in the work of a data scientist. Here is a guide to getting started with them.
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A Guide to Regularization in Python
Overfitting is a common problem data scientists face in their work. Here are two common and straightforward methods for resolving it.
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An Introduction to Dimensionality Reduction in Python
Dimensionality reduction is a vital tool for data scientists across industries. Here is a guide to getting started with it.
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A Guide to Data Cleaning in Python
In order to use data effectively for any kind of analysis, data scientists must be able to clean and prepare it first. Fortunately, Python makes doing so easy.
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A Guide to Time Series Forecasting in Python
Time series forecasting is a useful data science technique with applications in a wide range of industries and fields. Here’s a guide to getting started with the basic concepts behind it.
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An Introduction to Portfolio Optimization in Python
Python offers several straightforward techniques for putting together an optimized portfolio of investments. Here’s a guide to getting started with them.
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How to Form Clusters in Python: Data Clustering Methods
Every data scientist should know how to form clusters in Python since it’s a key analytical technique in a number of industries. Here’s a guide to getting started.
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A Guide to Outlier Detection in Python
Outlier detection is the process of identifying data points that have extreme values compared to the rest of the distribution. Learn three methods of outlier detection in Python.
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Mastering Machine Learning Explainability in Python
To make the most of machine learning for their clients, data scientists need to be able to explain the likely factors behind a model's predictions. Python offers multiple ways to do just that.
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A Guide to Time Series Analysis in Python
Time series analysis is a common task for data scientists. This guide will introduce you to its key concepts in Python.