Christopher Peters
Chief Technology Officer & Head Of AI at RIZZARR
Mobile, Alabama, United States
Summary
👤
Senior🎓
Top SchoolChristopher Peters is a data-driven technology leader and currently CTO & Head of AI at RIZZARR with 12 years of experience building data and AI capabilities that scale. He was Zapier’s ninth employee and first data scientist, helping grow the company to a multibillion-dollar valuation while founding multiple data and ML teams and embedding econometric thinking across the business. Trained as a Bayesian statistician with a Masters in Applied Statistics and a background in econometrics, he blends causal inference and economic strategy to translate complex models into profitable product and go-to-market decisions. He’s co-founded AI ventures and contributed to notable open-source projects—improving tutorials in Apache Airflow and hardening customer-lifetime modeling in the lifetimes library—showing a habit of making tools more usable for others. Based in Mobile, Alabama, he combines hands-on model building with technical leadership and systems design to position companies for scalable growth. Colleagues rely on him not only for analytic rigor but for a knack for turning statistical insight into clear business action.
12 years of coding experience
16 years of employment as a software developer
Masters of Applied Statistics, Masters of Applied Statistics at Louisiana State University
Stackoverflow
724reputation
40kreached
23answers
8questions
Github Skills (19)
apache-airflow10
learn-ruby-on-rails10
python10
data-science10
statistics10
user-manual10
workflow-engine10
basics10
documentation10
pandas9
numpy9
boxplot6
parallel-processing6
regression6
knitr6
Programming languages (9)
Github contributions (5)
CamDavidsonPilon/lifetimes
Jul 2017 - Jul 2017
Lifetime value in Python
Role in this project:
Data Scientist
Data ScientistContributions:6 commits, 6 PRs, 20 comments in 12 days
Contributions summary:Christopher's contributions focus on improving the `lifetimes` library, specifically related to customer lifetime value calculations. They implemented and tested the ParetoNBD model by adding unit tests to ensure consistency and protect against overflow errors. Further, the user improved the usability of the library by adjusting the handling of time periods and parameters for plots. Their commits also touched upon data preparation and summary data creation, highlighting their work with data analysis and model implementation.
lifetimestatisticspythondata-science
apache/airflow
Jun 2015 - Jun 2015
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:Technical Writer
Technical WriterContributions:2 PRs, 7 comments, 2 issues in 2 days
Contributions summary:Christopher's contributions primarily focus on improving the documentation for Apache Airflow's tutorial. They clarified terminology, such as the term "constructor" and "task," within the tutorial and example DAG files. The user made several typo fixes, improved readability and consistency in referring to tasks, and refined the explanation of parameters within the context of Jinja templating. Overall, the edits aim to enhance the clarity and understandability of the tutorial for new users.
monitorpythonschedulerapacheprogrammatically
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.