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# Data Scientist Supervisor 967

- **Company:** LTD Global
- **Location:** Los Angeles, CA, United States
- **Experience:** Experienced
- **Salary:** $114,400 - $124,800
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Data Analysis, Unit Testing, Microsoft Azure, Cloud Computing, Software Documentation, Cyber Security, Databases, Extract Transform Load (ETL), Data Security, Data Visualization, Database Development, R (Programming Language), Apache Hadoop, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Power BI, SQL Databases, Tableau (Software), Flask (Web Framework), Apache Spark, Backend, Pandas, Matplotlib, Scikit Learn, Data Analytics, Machine Learning Operations, Docker, Databricks
- **Published:** November 12, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-scientist-supervisor-967-los-angeles-ca--cd95f2bc-e1f3-429f-98c9-a6521c294c35

## About the Role

Data Science, Data Science, Database Programming, Data Analysis, Database Extract Transform and Load (ETL), Cloud Computing, Documentation, API Documentation, Quality Assurance, Information/Data Security (InfoSec), * 6 years of experience, including 3 years supervising data science teams and managing complex projects.

 * Experience supporting data-driven program, policy, and operational decisions.
 * Master's degree in Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, Public Health, or a related applied research field may substitute for 2 years of experience.

## Description

* Lead data science projects, database development, and technical teams.
 * Supervise and review the work of data scientists, analysts, and engineers.
 * Oversee ETL processes, backend pipelines, and cloud platforms (e.g., Databricks).
 * Lead code documentation and co-lead system, API, and ETL documentation.
 * Oversee data security, quality assurance, unit testing, and code integrity.
 * Apply machine learning, statistical analysis, and hypothesis testing techniques.
 * Use Python, R, SQL, Pandas, NumPy, Scikit-learn, Spark, and Hadoop.
 * Deploy models using Docker, Flask, AWS, or Azure.
 * Create data visualizations using Tableau, Power BI, Matplotlib, or Seaborn.