Stephen Klosterman - Digital Services For Nature-based Climate Solutions

Stephen Klosterman

Digital Services For Nature-based Climate Solutions

Santa Monica, California, United States

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Summary

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Senior

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Top School

Stephen Klosterman is a data scientist and PhD-led practitioner with 14 years of experience applying machine learning to environmental and commercial problems, currently leading digital services for nature-based climate solutions from Santa Monica. He has held senior and principal science roles at startups and enterprises—Tala, Earthshot Labs, CVS Health—and led science & technology for a tree-planting program, blending product-focused ML with ecosystem science. His academic background (PhD Harvard; MS Stanford) underpins hands-on work in remote sensing, time-series and image processing, and deployable models—evidenced by an unmanned aircraft system project and a chapter contribution to a practical data-science book. Stephen combines research rigor (Monte Carlo uncertainty, variance partitioning) with production impact (hospital readmission and outreach optimization models), and contributes to reproducible Python workflows exemplified by a Jupyter-based case study repo. Notably, he pairs quantitative modeling with field-forward conservation work, making him effective at translating ecological measurements into scalable climate solutions.

13 years of coding experience

10 years of employment as a software developer

MS Civil and Environmental Engineering - Hydrology, MS Civil and Environmental Engineering - Hydrology at University of Cincinnati

PhD Organismic and Evolutionary Biology, PhD Organismic and Evolutionary Biology at Harvard University

BA/BM Math/Music, BA/BM Math/Music at Oberlin College

MS Civil and Environmental Engineering - Atmosphere and Energy, MS Civil and Environmental Engineering - Atmosphere and Energy at Stanford University

Spanish, French, German

Github Skills (10)

pandas10

machine-learning10

jupyter-notebook10

data-exploration10

data-cleaning10

python10

data-science10

numpy9

scikit-learn9

scikit9

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

TrainingByPackt/Data-Science-Projects-with-Python

Jan 2019 - May 2020

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

Role in this project:

Data Scientist

Contributions:22 commits, 2 PRs, 20 pushes in 1 year 3 months

Contributions summary:Stephen primarily contributed to Chapter 1 of a data science project, working within a Jupyter Notebook environment. Their work focused on data loading and basic data integrity checks, including identifying and removing duplicate entries. The user then performed initial data exploration, specifically focusing on analyzing and cleaning features relevant to the dataset's core functionalities.

scikitpythonscienceknn-classificationclassification-algorithm

TrainingByPackt/Data-Science-Projects-With-Python-Second-Edition

Oct 2020 - May 2021

Contributions:26 commits, 26 pushes in 7 months

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