Christian Kendall is a Data Scientist at Salford Systems. He brings more than 4 years of research expertise, with a background in physical and life science emphasizing informatics and software development. Christian graduated with a Bachelor’s degree in Chemistry from Occidental College in Eagle Rock, CA, starting with a focus on biochemistry and bioinformatics that later turned into a passion for statistical data analytics and data science. As a researcher, Christian first saw and understood the need for practical modeling applications while working on automatic target recognition at NASA and then developing code for identifying proteins in high-throughput experiments later that same year in the Yates Laboratory at The Scripps Research Institute. At NASA, Christian fixed and optimized instruments while developing analytical methods for detecting bio-interest molecules. Christian also helped to design nanometer-scale structures for the study of photovoltaics while at the California Institute of Technology using 3D modeling and finite-difference time-domain solutions to simulate light absorption. His research continued at both the Mason Laboratory at Weill Cornell Medical College in New York, and The Scripps Research Institute in California, both with a focus on analysis and preparation of DNA sequencing libraries for genomics and metagenomics. Christian’s continued interest in data, automation, software development led him to Salford Systems as a Data Scientist, where he implements machine learning and data mining techniques with our proprietary software to create practical applications for real-world problems. When he’s not crunching numbers, Christian enjoys cooking and baking, brewing kombucha, and trying to keep a lot of cacti and flowers alive.
If you search for top skills that data scientists need, you will find communication in the top five or ten in every post, right up there with technical skills...
Blog posts like “Reservoir Sampling and Neural Networks for Streaming Data and the IoT” or “Up-Lift Modelling for Cancer Treatments” are interesting and useful to data scientists in particular fields or to particularly creative and savvy readers. However, starting out with esoteric use cases or technical descriptions of specific technologies severely limits the “what,” “why,” and “how” of analysis, if not leaving it out altogether. Leading with current results and building value first is a boon to everyone.
Copyright © 2017 Salford Systems