> Markdown version of [/jobs/ext/4669307-data-science-supervisor](https://www.wearedevelopers.com/jobs/ext/4669307-data-science-supervisor). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Science Supervisor

- **Company:** HonorVet Technologies
- **Location:** Los Angeles, CA, United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Business Logic, Unit Testing, Microsoft Azure, Big Data, Cloud Computing, Software Documentation, Databases, Information Engineering, Extract Transform Load (ETL), Data Security, Data Visualization, Data Warehousing, Relational Databases, Database Development, R (Programming Language), Apache Hadoop, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Scrum Methodology, Power BI, SQL Databases, Tableau (Software), Cloud Platform System, Flask (Web Framework), Apache Spark, Backend, Pandas, Matplotlib, Scikit Learn, Data Analytics, Api Design, Data Pipelines, Docker, Unsupervised Learning, Databricks
- **Published:** November 11, 2026
- **Apply:** https://www.dice.com/job-detail/676f7e7b-9a29-4615-a04e-74c70a985c10

## About the Role

* Strong hands-on experience with Python, R, and SQL.
 * Proficiency in data science libraries and frameworks, including Pandas, NumPy, and Scikit-learn.
 * Experience with machine learning techniques, including supervised and unsupervised learning.
 * Strong understanding of statistical analysis and hypothesis testing.
 * Experience with big data technologies such as Apache Spark and Hadoop.
 * Hands-on experience with ETL processes, backend data pipelines, relational databases, and data warehousing.
 * Experience working with cloud platforms such as AWS, Azure, and Databricks.
 * Experience deploying machine learning models using Docker, Flask, or cloud-based platforms.
 * Advanced data visualization skills using Tableau, Power BI, Matplotlib, Seaborn, or similar tools.
 * Knowledge of API design, system documentation, data security, and quality assurance practices.
 * Experience with Agile/Scrum methodologies and technical project coordination.

Experience Requirements

 * Minimum of 6 years of relevant professional experience.
 * At least 3 years of experience supervising a team of data science professionals.
 * Demonstrated experience serving as a subject matter expert and coordinating complex data science projects.
 * Experience supporting program, policy, and operational decision-making through data science solutions.

Education Requirements

 * Master's degree from an accredited college or university in Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, Public Health, Applied Research, or a related field.
 * A qualifying Master's degree may substitute for up to 2 years of the minimum required professional experience.

## Description

We are seeking an experienced Data Science Supervisor to lead a team of data science professionals while providing hands-on technical leadership across data science, data engineering, ETL pipelines, and cloud environments.
The ideal candidate will have strong expertise in machine learning, statistical analysis, relational databases, Python, R, SQL, and data visualization. This role requires the ability to supervise technical teams, oversee complex data science projects, establish data quality and security standards, and support data-driven program, policy, and operational decision-making.
The Data Science Supervisor will collaborate with data scientists, analysts, and engineering teams to guide technical strategy, manage project deliverables, and drive Agile/Scrum activities., * Supervise and mentor data science professionals while coordinating complex data science projects.

 * Provide technical leadership for database development, ETL processes, backend pipelines, and cloud environments such as Databricks.
 * Oversee data engineering activities, pipeline design, data warehousing, and database architecture.
 * Lead technical and code-level documentation while contributing to system documentation, API layouts, and ETL business logic.
 * Establish and oversee data security implementation, quality assurance, unit testing, and code integrity standards.
 * Guide the development and implementation of machine learning models using supervised and unsupervised learning techniques.
 * Oversee statistical analysis, hypothesis testing, regression, classification, clustering, and dimensionality reduction.
 * Support model deployment using Docker, Flask, and cloud platforms such as AWS and Azure.
 * Review technical deliverables and ensure data science solutions meet business and operational requirements.
 * Participate in Agile/Scrum activities, including sprint planning, daily stand-ups, technical feasibility assessments, and blocker resolution.
 * Coordinate deployment activities, change management, and technical deliverables across project teams.
 * Collaborate with stakeholders to translate business requirements into scalable technical solutions.