Data Scientist Supervisor
Location: Los Angeles, California
Work Arrangement: Hybrid
Contract Duration: 1-year, with possible extension, W2
Pay: $56.00/hr
Bring your data science leadership to a mission-driven public sector environment. In this role, you’ll guide a team delivering advanced analytics and machine learning that support program, policy, and operational decision-making, while also staying hands-on with modern data platforms and engineering practices.
Overview
We’re seeking a Data Scientist Supervisor to serve as both a technical and people leader. You’ll supervise data science professionals, lead complex projects where the path forward may be ambiguous, and partner closely with engineering to review work products and drive high-quality delivery. This is a temporary contract assignment based in Los Angeles, CA (Hybrid).
Key Responsibilities
- Supervise and mentor a team of data scientists and analysts while coordinating complex analytics initiatives.
- Lead ETL, backend pipelines, and cloud environments (including Databricks).
- Drive technical and code-level documentation; co-lead system documentation, API layouts, and ETL business logic.
- Own quality assurance practices, including unit testing and code integrity protocols.
- Lead data security implementation from a technical standpoint.
- Apply machine learning (supervised/unsupervised) and statistical methods to real-world problems.
- Participate in Agile/Scrum workflows, supporting sprint planning, daily stand-ups, blocker removal, and delivery commitments.
Must-Have Qualifications
- 6+ years of experience, including 3+ years supervising data science professionals and overseeing complex data science projects.
- Hands-on expertise with Python, R, and SQL.
- Experience with machine learning (regression, classification, clustering, dimensionality reduction), statistical analysis, and hypothesis testing.
- Proficiency with tools such as Pandas, NumPy, scikit-learn.
- Experience with big data frameworks such as Apache Spark and/or Hadoop.
- Experience deploying models using Docker and/or Flask and/or cloud platforms (AWS, Azure).
- Strong data visualization skills (e.g., Tableau, Power BI, Matplotlib, Seaborn).
- Livescan required.
Nice-to-Have
- Strong background in data engineering practices, pipeline design, and data warehousing.
- Prior experience acting as a technical lead for documentation, QA, and security implementation.
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