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Provider Data Operations Jobs in California (NOW HIRING)

... Provide input into team-level goals by synthesizing insights from data operations performance and translating them into actionable recommendations for AI product teams • Adapt program plans in ...

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Provider Data Operations information

What are the key skills and qualifications needed to thrive in provider data operations?

To excel in Provider Data Operations, you need strong analytical skills, attention to detail, and experience with healthcare data management, often supported by a bachelor's degree in a related field. Familiarity with provider data management systems, claims processing software, and tools like Excel or SQL is typically required. Excellent communication, problem-solving abilities, and organizational skills help professionals collaborate effectively and resolve data discrepancies. These competencies ensure accurate provider information, regulatory compliance, and seamless healthcare operations.

What is provider data operations?

Provider Data Operations refers to the processes involved in managing and maintaining accurate information about healthcare providers within an organization. This includes collecting, verifying, updating, and organizing data such as provider credentials, specialties, contact information, and practice locations. These operations are essential for ensuring that provider directories are current, claims are processed correctly, and regulatory requirements are met. Efficient provider data management supports better patient care, reduces administrative errors, and helps organizations comply with industry standards.

What is the difference between Provider Data Operations vs Provider Data Analysts?

AspectProvider Data OperationsProvider Data Analysts
Primary FocusManaging and maintaining provider data systems, workflows, and data integrityAnalyzing provider data to generate insights, reports, and support decision-making
Required SkillsData management, system administration, attention to detailData analysis, reporting, statistical skills
Work EnvironmentData management teams, healthcare IT departmentsAnalytics teams, healthcare business units
CertificationsData management certifications, healthcare IT credentialsData analysis certifications, healthcare analytics training

Provider Data Operations primarily focuses on maintaining and managing provider data systems and workflows, ensuring data accuracy and integrity. Provider Data Analysts analyze provider data to generate insights and support strategic decisions. While both roles work with provider data, Operations emphasizes data management processes, whereas Analysts focus on data interpretation and reporting.

What are some common challenges faced in provider data operations, and how are they typically addressed?

Provider Data Operations professionals often encounter challenges such as maintaining accurate and up-to-date provider information, ensuring compliance with regulatory requirements, and coordinating data across multiple systems or departments. To address these challenges, teams frequently use robust data management software, implement regular audits, and collaborate closely with IT, compliance, and provider relations teams. Continuous process improvement and clear communication are also essential to minimize errors and streamline workflows.
Infographic showing various Provider Data Operations job openings in California as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Product Data Operations Program Manager

Meta

Menlo Park, CA • On-site

$103K - $155K/yr

Full-time

Posted 3 days ago

New


Meta rating

7.8

Company rating: 7.8 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

136th of 242 rated software companies


Job description

Meta is seeking a Product Data Operations Program Manager to drive AI solutions and data programs that power intelligent products across Meta's portfolio. In this role, you will manage end-to-end data operations programs that support AI model development, training data pipelines, and data quality initiatives - enabling teams to build and ship AI-driven features at scale. You will partner with data science, engineering, product, and operations teams to define program strategies, resolve data pipeline dependencies, and ensure high-quality data outputs that directly influence AI product outcomes.
Responsibilities
Manage and deliver data operations programs that support AI model training, evaluation, and deployment pipelines across product teams
• Partner with data science, engineering, and product teams to define data requirements, prioritize data collection efforts, and align on quality standards for AI solutions
• Identify and resolve bottlenecks in data labeling, annotation, and curation workflows to ensure timely delivery of high-quality training datasets
• Analyze complex data operations challenges and propose scalable solutions that align with AI product roadmaps and organizational goals
• Develop and maintain program documentation, including data governance frameworks, workflow specifications, and milestone tracking for AI data initiatives
• Engage team leaders and cross-functional stakeholders to build alignment on program direction, surface risks early, and drive decisions that unblock data operations work
• Leverage AI tools and workflow automation to improve the efficiency and quality of data operations processes, sharing learnings to scale adoption across the team
• Track and communicate program health metrics - including data throughput, quality rates, and delivery timelines - to stakeholders at varying leadership levels
• Provide input into team-level goals by synthesizing insights from data operations performance and translating them into actionable recommendations for AI product teams
• Adapt program plans in response to shifting AI product priorities, regulatory requirements, or data availability constraints, maintaining focus on highest-impact deliverables
Minimum Qualifications
• 2+ years of experience in program management, data operations, or technical operations roles supporting AI, machine learning, or data-driven product development
• Experience managing cross-functional programs involving data pipelines, data labeling, annotation workflows, or AI training data quality initiatives
• Experience analyzing operational data and communicating findings and recommendations to technical and non-technical stakeholders
• Experience building and maintaining program tracking systems, documentation, and reporting frameworks for complex, multi-team initiatives
• Experience identifying process inefficiencies and implementing scalable solutions within data or AI operations environments
Preferred Qualifications
• Familiarity with data governance practices, metadata management, or compliance considerations relevant to AI training data
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience working directly with data science or machine learning teams to define data requirements and evaluate dataset quality for AI model development
• Experience managing vendor or outsourced data annotation and labeling operations at scale
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience using AI-powered tools or workflow automation platforms to redesign and accelerate data operations processes
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
About Meta
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today-beyond the constraints of screens, the limits of distance, and even the rules of physics.
Equal Employment Opportunity
Meta is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. You may view our Equal Employment Opportunity notice here.

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