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Jay Oliver

@jayoliver

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Applied Data Scientist at Leidos who identified high-confidence orphaned well candidates across millions of records.

United States

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At Leidos, I developed Python and SQL ETL pipelines that brought legacy files, databases, and operational data into scalable relational models. I also automated ingestion and transformation workflows, reducing processing effort by approximately 90%.

I developed machine learning workflows for orphaned well analysis, including models that identified high-confidence candidates across millions of records. At Baylor University, I was the primary Python developer for an open-source project supporting a peer-reviewed publication in Scientific Data.

Experience

Work history, roles, and key accomplishments

Geospatial Data Scientist

Leidos

Feb 2024 - May 2026 (2 years 3 months)

Designed modular Python and SQL ETL pipelines integrating legacy and operational datasets into scalable relational data models. Developed machine learning pipelines analyzing over 7 million oil and gas well records to identify orphaned well candidates.

Python SQL ETL Pipelines REST API Integration Machine Learning scikit learn XGBoost Random Forest Feature Engineering Data Modeling Azure Storage Git Linux BASH

Lab Manager / Geospatial Data Scientist

Baylor University

Apr 2023 - Feb 2024 (10 months)

Served as technical lead across analytics and data science initiatives, coordinating project execution and workflow development. Developed Python-based machine learning and analytical pipelines integrating REST APIs, SQL databases, and flat-file data sources.

Python Machine Learning REST API Integration SQL ETL Data Visualization Open Source Development Workflow Automation

Geospatial Data Specialist

Baylor University

Oct 2021 - Apr 2023 (1 year 6 months)

Built scalable data pipelines and machine learning workflows supporting transportation and environmental research. Integrated remote sensing products, APIs, and socioeconomic datasets into Python-based ETL workflows for predictive modeling.

Python ETL Machine Learning Random Forest Remote Sensing OpenCV Selenium Data Pipelines predictive modeling SQL

Spatial Analyst

U.S. Geological Survey

Jan 2021 - Jul 2021 (6 months)

Developed Python and R analytical workflows to process and visualize over 20 years of environmental datasets, generating temporal trend analyses and reproducible land-cover change assessments.

Python R Data Processing Data Visualization Temporal Reproducible Research

Data Scientist / Statistical Analyst

World Wildlife Fund

Jan 2020 - Dec 2020 (11 months)

Integrated and standardized multi-source geospatial, socioeconomic, and biodiversity datasets for spatial trend analysis. Developed logistic regression modeling workflows for large-scale spatial prediction tasks.

Python R Logistic Regression Geospatial analysis Data Integration Statistical Modeling Statistical Analysis

Remote Sensing Data Scientist

NASA

Sep 2019 - Apr 2020 (7 months)

Developed Python-based ETL workflows in a Linux environment to automate ingestion and preprocessing of over 900 multidimensional datasets. Engineered scalable analytical workflows for large time-series data processing.

Python ETL Linux Data Processing Time Series Analysis Workflow Automation