Data Scientist

University of California - Los Angeles Health; Los Angeles (CA); Hybrid; USD 105,700 - 234,500; Full time

Job summary

University of California - Los Angeles Health is seeking a Data Scientist to transform healthcare using AI/ML, enhance MLOps/LLMOps, and lead responsible AI initiatives. You will drive projects end-to-end and communicate insights to clinical, operational, and executive stakeholders.

The role requires strong expertise in LLMs, AI governance, and healthcare analytics, with a flex-hybrid on-site requirement in the Greater Los Angeles area.

Qualifications

• Extensive hands-on experience with LLMs and generative AI techniques.
• Strong understanding of MLOps, LLMOps, governance, and bias testing.

Responsibilities

• Lead AI/ML initiatives across UCLA Health system from framing problems to delivering outputs.
• Enhance MLOps, LLMOps & Responsible AI governance across models.
• Conduct bias and fairness testing for models.
• Interpret model outputs and communicate insights to stakeholders.
• Foster collaboration across departments and drive actionable work plans.
• Leverage LLMs, generative AI, and agentic AI frameworks.
• Identify AI/ML opportunities addressing key business challenges.

Skills

LLMs & Generative AI

MLOps & LLMOps

Bias & Fairness Testing

Python

R

SQL

Shell Scripting

C++

Java

Data Visualization

Communication with Stakeholders

Education

Master's degree in CS/Math/Statistics/Engineering or related field

PhD preferred

Tools

TensorFlow

Keras

scikit-learn

Azure

Databricks

Jupyter Notebooks

iPython Notebooks

Job description

Transform Healthcare with Cutting-Edge AI and ML Technologies

Are you passionate about transforming healthcare through cutting-edge AI and ML technologies? We are seeking a highly skilled Data Scientist to drive innovative initiatives and improve healthcare outcomes. This role offers an exciting opportunity to apply your expertise in AI/ML, enhance our MLOps and LLMOps frameworks, and contribute to the responsible use of AI across our health system.

Key Responsibilities:

• Lead AI/ML Initiatives:Develop, evaluate, and validate AI/ML models that support and enhance clinical, operational, and financial processes across the UCLA Health system. Lead projects end to end, from framing the problem to delivering high-quality outputs on schedule.
• Enhance MLOps, LLMOps & Responsible AI Governance:Apply and advance our ML and LLM operations paradigms and uphold our AI governance framework, ensuring ethical and scalable AI practices are integrated into every model developed and deployed.
• Bias & Fairness Testing:Conduct rigorous statistical bias and fairness evaluation strategies for models developed by UCLA Health teams and external vendors, ensuring equitable AI solutions that reflect the diversity of our patient population.
• Deliver Actionable Insights:Interpret model outputs and effectively communicate insights to stakeholders at various technical levels. Use strong storytelling, visualization, and communication skills to drive alignment and impact across clinical, operational, and executive audiences.
• Drive Collaboration & Innovation:Foster a culture of collaboration across departments by sharing knowledge, best practices, and new developments in AI/ML. Operate effectively in agile, cross-functional teams and help structure clear, actionable work plans to guide team execution.
• Leverage Advanced AI/ML Techniques:Utilize large language models (LLMs), generative AI, and agentic AI frameworks to solve complex healthcare challenges in an ever-evolving technological landscape.
• Identify AI/ML Solutions for Stakeholder Needs:Collaborate with clinical, financial, and operational teams to identify AI/ML opportunities that address key business challenges and improve outcomes. Guide project scoping and execution to ensure relevance, feasibility, and impact.

Seeking a candidate with:

• Extensive hands‑on experience with large language models (LLMs) and generative AI techniques
• Strong understanding of MLOps, LLMOps, responsible AI governance, and bias/fairness testing methodologies
• Excellent communication and stakeholder engagement skills, with the ability to explain complex technical concepts and influence decision‑making
• Demonstrated experience leading data science projects with structured work plans, clear milestones, and timely, high‑quality deliverables
• Deep knowledge of healthcare systems and an understanding of clinical, financial, and operational challenges in the healthcare industry
• Ability to work in an innovation‑driven, agile environment, continuously learning and applying the latest AI/ML technologies with strong statistical and experimental foundations

Additional Information:

• Epic Certification:Selected candidates will be required to complete Epic certifications within 6 months of hire if not currently certified.
• Selection Timeline:We will be reviewing applications through August 2026.

This is a flex‑hybrid role requiring presence on‑site at least 20% of the time, and as needed based on operational requirements. Candidates must live in the Greater Los Angeles area or be willing to relocate. Please note, travel to the "home office" location is not reimbursed. Each employee will complete a FlexWork Agreement with their manager to outline expectations and ensure mutual understanding. These arrangements are periodically reviewed and may be adjusted or terminated as necessary.

Salary offers are based on a variety of factors including qualifications, experience, and internal equity. The full salary range for this position is $105,700-$234,500 annually. The University anticipates offering a salary between the minimum and midpoint of this range.

Qualifications

Technical Skills

• Master's degree in Computer Science, Mathematics, Statistics, Engineering, or other computational/quantitative field is required. PhD is preferred.
• 2 or more years of experience in advanced analytics, statistical modeling, and code development, including expertise in neural networks, deep learning, NLP, supervised and unsupervised learning, and frameworks like TensorFlow, Keras, and scikit‑learn.
• Demonstrated expertise in leveraging advanced AI techniques, including LLMs and generative AI, to address complex challenges such as predictive analytics and content extraction.
• Experience with Microsoft Azure or similar cloud‑based technologies and analytics platforms such as Databricks is preferred.
• Proficiency in Python or R is required, and experience using analytical documentation and development tools, including Jupyter Notebooks, Databricks, or iPython notebooks, to structure and present analyses is preferred.
• Strong programming skills, including shell scripting, Python, Perl, C++, SQL, and Java, are preferred.
• Proficiency in documenting workflows, methodologies, and assumptions to support MLOps practices and ensure transparency, reproducibility, and ethical AI practices.
• Experience with data visualization tools like Tableau, Power BI, matplotlib, or ggplot2 is required.
• Experience performing statistical analysis to quantify model limitations and ensure ethical AI practices, including bias and fairness testing.

Data Management

• Experience with healthcare data and/or EHR data is preferred.
• Strong metadata management skills, with the ability to synthesize and analyze large datasets, identify patterns, and integrate structured and unstructured data for model development.
• Demonstrated experience synthesizing data to produce actionable recommendations and optimize objectives.
• Organizational and Communication Skills
• Excellent written and verbal communication skills, with the ability to explain complex quantitative models to stakeholders at all levels.
• Proven problem‑solving skills, including identifying root causes, evaluating solutions, and delivering data‑driven outcomes for organizational objectives.
• Exceptional collaboration skills, including active listening, rapport building, consensus building, and effective delegation.
• Experience organizing work, generating task lists, balancing multiple projects, and effectively reporting progress is required.
• Strong organizational and interpersonal skills to thrive in a collaborative and fast‑paced environment.

Leadership and Team Skills

• Ability to transfer knowledge and concepts to implementation teams and mentor team members.
• Strong staff development, leadership, and coaching skills.
• Demonstrated ability to influence stakeholders, lead discussions, and present findings to clinical and business leaders across the organization.
• Ability to identify, document, and resolve issues effectively while maintaining an organized issues log.
• High‑functioning team skills with the ability to balance multiple competing tasks efficiently.