Sadrach Pierre
Senior Data Scientist at a hedge fund based in New York City

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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A ball python drapes itself over an empty picture frame. /software-engineering-perspectives/define-empty-variables-python
Sep 08, 2022
Missing values in large datasets are a common problem, and dealing with them is an essential skill for every programmer.
A front-loader truck pours sand into a large pile. /software-engineering-perspectives/heapify-heap-tree-c++
Aug 23, 2022
A step-by-step tutorial on how to heapify data, including visuals and example code.
A hand stops dominoes from falling
Jul 07, 2022
Exception handling is vital for producing code that functions properly under unusual conditions or, at a minimum, clearly explains errors to a user. This guide will introduce you to its principles in C++.
A visual representation of data
Jun 14, 2022
Having a thorough understanding of Python lists and NumPy arrays opens the door to many useful data tasks. This guide will introduce you to both concepts.
A data scientist works on multiple computer monitors
May 31, 2022
Quartiles are a useful tool to rigorously analyze various types of data. This guide will introduce you to their calculation in Python.
A closeup picture of a python's skin.
May 10, 2022
Profiling is a crucial tool for data scientists to be able to analyze bottlenecks in a process and ensure smooth, efficient operation. This guide will help you get started with profiling tools in Python.
A group of people walks down one path while a single person chooses a second path
Apr 27, 2022
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.
A tech worker at a computer terminal with a data overlay
Feb 28, 2022
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.
A selection bracket
Jan 25, 2022
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.
A technical overlay as a person looks at a globe
Nov 30, 2021
Overfitting is a common problem data scientists face in their work. Here are two common and straightforward methods for resolving it.
A visual representation of data
Nov 12, 2021
Dimensionality reduction is a vital tool for data scientists across industries. Here is a guide to getting started with it.
Oct 25, 2021
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.