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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Now that Excel enables Python logic within its interface, a wide variety of professionals can draw on the power of Python tools to supplement their day-to-day analytics workflows.
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Pareto analysis, which strives to separate a vital few factors in a problem from an insignificant many, has a wide variety of applications across industries. Our expert shows you how it works.
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Python virtual environments help manage software dependencies and ensure code is reproducible. Our expert explains how they work and shows you how to get started with them.
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Class inheritance is an important concept in Python for data scientists and machine learning engineers to know. Here, our expert explains how it works.
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A function is a named section of code that performs a specific task. Here, our expert introduces you to how they work in Python.
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In Python, function wrappers are called decorators, and they have a variety of useful applications in data science. This guide covers how to use them for managing model runtime and debugging.
A ball python drapes itself over an empty picture frame. /software-engineering-perspectives/define-empty-variables-python
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++
A step-by-step tutorial on how to heapify data, including visuals and example code.
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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++.
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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.
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Quartiles are a useful tool to rigorously analyze various types of data. This guide will introduce you to their calculation in Python.
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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.