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
financial data analysis
Need to Perform Financial Data Analysis? Why Python Is Your Best Tool.
Python offers a wide array of tools to access, analyze and pull insights from financial data. Here’s how to get started.
machine-learning-models-python
A Guide to Selecting Machine Learning Models in Python
Data scientists need to have a good understanding of how to select the best features when it comes to model building. This guide will help you get started.
streamlit-tutorial
Streamlit Tutorial: A Beginner’s Guide to Building Machine Learning-Based Web Applications in Python
Analytics dashboards are a great way for data scientists to communicate insights to companies, but they can often be expensive and time-consuming to build. Streamlit is an easy-to-use library for Python that simplifies the process.
evaluating-classification-models
How to Evaluate Classification Models in Python: A Beginner's Guide
This guide introduces you to a suite of classification performance metrics in Python and some visualization methods that every data scientist should know.
text-data-wrangling
A Beginner’s Guide to Using Pandas for Text Data Wrangling With Python
Mastering the basic Pandas tools and skill sets is important for generating the type of clean and interpretable text data that allows for insights. Here’s a guide to getting started.
covid-vaccine-sentiment
How Do People Feel About the COVID Vaccine?
Sentiment analysis is an AI-based task for determining popular attitudes about various subjects. Let’s use it to gauge attitudes toward the various COVID vaccines.
graphs and charts visualizing data
Python Data Visualization With Seaborn and Matplotlib
Every data scientist needs to know how to create data visualizations to communicate their research. Here’s how to start in Python.
guide-scikit-learn
A Comprehensive Guide to Scikit-Learn (Sklearn)
Scikit-learn is a powerful machine learning library that’s a great place for beginners to get their feet wet. Here’s a guide to getting started with it.
Python Code
Use the Django Framework to Rapidly Develop Web Apps
Django is a user-friendly web framework in Python that’s widespread in its use. Here’s an overview of what makes it so useful.
A visualization of big data
Why Keras Is the Leading Deep Learning API
Keras’ user-friendly design means that, even with very little experience, researchers can usefully put it to work across a variety of fields.
A doctor uses an iPad for work
How Artificial Intelligence Can Revolutionize Healthcare
Generative Adversarial Networks offer doctors and healthcare organizations a range of applications in patient treatment and privacy protection.
A magnifying glass on a job ad
Using Google's BERT to Battle Job Scams
The BERT model has many practical applications. Here, we put it to work solving an employment scam.