Data Scientist Supervisor

Salary Range: $9,852.82 - $13,278.10 monthly

SUMMARY

The Data & Analytics Unit is responsible for collecting, analyzing, and interpreting healthcare data to support decision-making across the Los Angeles County Department of Health Services (DHS).

The unit manages patient care and operational data, using analytics to identify patterns, predict outcomes, and improve service delivery.

The unit also ensures data integrity, security, and regulatory compliance.

The Data Scientist Supervisor leads a team of data scientists within the DHS Data & Analytics Unit, overseeing the execution of advanced analytical projects, machine learning initiatives, and data-driven strategies.

This role is responsible for managing the development and implementation of predictive models, optimizing data workflows, and ensuring that analytical solutions align with organizational objectives.

The Data Scientist Supervisor provides technical leadership, mentors team members, and fosters a collaborative environment to drive innovation and efficiency in data science operations.

ESSENTIAL FUNCTIONS
1.

Team Leadership & Mentorship - Lead, coach, and mentor data scientists, fostering a culture of continuous learning and professional growth.
2.

Project Oversight & Execution - Manage and oversee the development of machine learning models and analytical solutions to meet business needs.
1.

Apply advanced statistical methods, machine learning algorithms, and data mining techniques to analyze large and varied datasets, uncovering trends and patterns that provide actionable insights.
2.

Fine-tune and optimize models, ensuring they are scalable, efficient, and aligned with business requirements.
3.

Mentor junior data scientists and guide their model development, statistical analysis, and data science practices.
3.

Data Engineering & Workflow Optimization - Collaborate with engineering teams to ensure the scalability, efficiency, and accuracy of data pipelines.
4.

Quality Assurance & Best Practices - Establish and enforce best practices in data science methodologies, model validation, and documentation.
5.

Advanced Data Analysis & Modeling
6.

Lead the development of predictive, prescriptive, and diagnostic models to address complex business problems and optimize decision-making processes.
7.

Machine Learning & AI Implementation - Design, train, and optimize machine learning models for forecasting, anomaly detection, and automation.
8.

Insightful Reporting & Visualization
1.

Create and deliver high-quality, clear, and actionable reports and dashboards, translating complex data findings into easily understandable insights for both technical and non-technical stakeholders.
2.

Use advanced visualization tools and techniques to convey analytical results effectively to leadership and business teams.
3.

Develop and implement metrics and KPIs that measure the effectiveness of data science initiatives and m...