Data Scientist Supervisor at Public Health Foundation Enterprises, In in Los Angeles, California

Job Description:

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 model performance.
   
   
   
   


9. Strategic Collaboration & Stakeholder Engagement
   
   
   
   1. Work closely with business leaders and stakeholders to define data-driven strategies and deliver actionable insights.
   
   
   2. Translate complex technical concepts into actionable business insights and recommendations for non-technical audiences.
   
   
   3. Partner with IT, engineering, and business teams to integrate data science solutions into operational processes.
   
   
   
   


10. Continuous Improvement & Research
    
    
    
    1. Stay abreast of emerging data science techniques, industry trends, and technologies to drive innovation within the team and ensure best-in-class data science practices.
    
    
    2. Lead research initiatives that explore new methods for data analysis, modeling, and automation.
    
    
    3. Continuous improvement of data science workflows, techniques, methodologies, new tools, and technologies within the organization.
    
    
    
    

JOB QUALIFICATIONS

The ideal candidate is a seasoned data science professional with strong leadership skills, a track record of managing data science projects, and the ability to translate complex analytics into strategic business recommendations. They should possess a combination of technical expertise, team management experience, and a deep understanding of machine learning and data-driven decision-making.

Education/Experience

• Master’s degree from an accredited institution in Data Science, Computer Science, Statistics, Mathematics, or a related field.


• 6+ years of experience in data science, with at least 2 years in a leadership role.


• Extensive hands-on experience in machine learning, AI, and predictive modeling. Proven experience leading and mentoring data science teams.

Certificates/Licenses/Clearances

• Successful clearing through the Live Scan and health clearance processes with the County of Los Angeles.

Other Skills, Knowledge, and Abilities

• Advanced programming skills in Python, R, or SQL for model development and data processing.


• Expertise in cloud computing (AWS, Azure, GCP) and big data technologies.


• Strong experience with data visualization tools (Tableau, Power BI) for storytelling and reporting.


• Deep knowledge of data engineering concepts, ETL processes, and model deployment.


• Proven ability to lead data science projects from ideation to implementation.


• Excellent communication skills to present complex insights to both technical and non-technical audiences.


• Experience mentoring junior data scientists and fostering a data-driven culture.


• Strong understanding of agile methodologies and project management principles.


• Commitment to innovation and staying at the forefront of industry trends.

PHYSICAL DEMANDS 

Stand: Not applicable

Walk: Not applicable

Sit: Frequently

Handling / Fingering: Constantly

Reach Outward: Constantly

Reach Above Shoulder: Not applicable

Climb, Crawl, Kneel, Bend: Not applicable

Lift / Carry: Occasionally - Not applicable

Push/Pull: Occasionally - Not applicable

See: Constantly

Taste/ Smell: Not Applicable

Not Applicable = Not required for essential functions

Occasionally = (0 - 2 hrs/day)

Frequently = (2 - 5 hrs/day)

Constantly = (5+ hrs/day)

WORK ENVIRONMENT

Hybrid

EEOC STATEMENT

It is the policy of Heluna Health to provide equal employment opportunities to all employees and applicants, without regard to age (40 and over), national origin or ancestry, race, color, religion, sex, gender, sexual orientation, pregnancy or perceived pregnancy, reproductive health decision making, physical or mental disability, medical condition (including cancer or a record or history of cancer), AIDS or HIV, genetic information or characteristics, veteran status or military service.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities

The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c)

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