Morgan Sell
Manager, Data Science & Engineering
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolMorgan Sell is a Manager of Data Science & Engineering in Los Angeles with 11 years of experience blending project finance, asset management, and advanced analytics to deliver production-grade ML, data engineering, and GenAI solutions. He leads small technical teams while remaining a hands-on architect and developer, guiding clients from mid-market firms to public companies through architecture, deployment, and senior stakeholder engagement. Early career roots in structured finance and roles advising the DOE and CFO/CEOs give him a rare fluency in financial modeling, time-series forecasting, and risk-aware decision making. As a full-stack data scientist and open-source contributor, he has improved feature-engine by adding robust NaN detection, time-series lag transformers, and regression-capable encoders that strengthen real-world data pipelines. Comfortable across the stack—from ETL and Snowflake-style schemas to experimentation and behavioral science—he focuses on practical, auditable solutions that lower costs and preserve outcomes. Colleagues describe him as a pragmatic technical leader who converts domain expertise into dependable, production-ready analytics.
11 years of coding experience
13 years of employment as a software developer
Master of Science International Management, Master of Science International Management at EsadeMaster of Arts International Business, Master of Arts International Business at University of Florida
English, Spanish
Stackoverflow
99reputation
13kreached
1answer
14questions
Github Skills (20)
python10
data-science10
scikit10
pandas10
machine-learning10
feature-engineering10
scikit-learn10
data-validation9
time-series9
data-analysis9
pytest8
feature-extraction7
anaconda6
dask6
amazon-s36
Programming languages (3)
Github contributions (5)
feature-engine/feature_engine
Dec 2021 - Aug 2022
Feature engineering package with sklearn like functionality
Role in this project:
Data Scientist
Data ScientistContributions:79 reviews, 80 commits, 23 PRs in 7 months
Contributions summary:Morgan's primary focus was on enhancing the feature engineering capabilities of the `feature_engine` library. Their contributions included adding NaN detection to base encoders and discretizers, thereby improving data validation and error handling. They also implemented a new TimeSeriesLagTrasnformer class for time-series feature engineering. In addition, the user modified the DecisionTreeEncoder and Discretizer by adding regression functionality and error handling.
pythonfeature-extractiondata-sciencesklearnmachine-learning
Morgan-Sell/feature_engine
Dec 2021 - Mar 2024
Feature engineering package with sklearn like functionality
Contributions:7 reviews, 10 PRs, 697 pushes in 2 years 3 months
pythondata-sciencesklearnmachine-learningengineering
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