Senior Data Scientist

Heluna Health•Los Angeles, CA

11h•Hybrid

About The Position

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 Senior Data Scientist manages advanced analytical projects, developing sophisticated machine learning models, and providing strategic insights to drive data-informed decision-making. This role requires expertise in statistical modeling, data engineering, and predictive analytics, as well as the ability to mentor junior data scientists and collaborate with cross-functional teams. The Senior Data Scientist plays a key role in designing scalable data solutions, optimizing analytical workflows, and ensuring alignment between data initiatives and organizational goals.

Requirements

Bachelor’s or Master’s degree from an accredited institution in Data Science, Computer Science, Statistics, Mathematics, or a related field.
4+ years of experience in data science, advanced analytics, or a related role.
Extensive hands-on experience in machine learning, AI, and predictive modeling.
Successful clearing through the Live Scan process with the County of Los Angeles.
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 complete 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.

Responsibilities

Manage the development of predictive, prescriptive, and diagnostic models to address complex business problems and optimize decision-making processes.
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.
Fine-tune and optimize models, ensuring they are scalable, efficient, and aligned with business requirements.
Mentor junior data scientists and guide their model development, statistical analysis, and data science practices.
Design, train, and optimize machine learning models for forecasting, anomaly detection, and automation.
Collaborate with engineering teams to improve data pipelines, ensure data integrity, and enhance model deployment.
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.
Use advanced visualization tools and techniques to convey analytical results effectively to leadership and business teams.
Develop and implement metrics and KPIs that measure the effectiveness of data science initiatives and model performance.
Work closely with business leaders and stakeholders to define data needs and prioritize projects that align with organizational goals and objectives.
Translate complex technical concepts into actionable business insights and recommendations for non-technical audiences.
Collaborate with cross-functional teams, including product, engineering, and IT, to design and implement data solutions that drive business impact.
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.
Research initiatives that explore new methods for data analysis, modeling, and automation.
Contribute to the continuous improvement of data science workflows, tools, and methodologies within the organization.