Role Description We’re looking for a driven, detail‑oriented individual to join our team as a Prior Authorization Specialist. You’ll receive training and ongoing support to set you up for success, along with a performance bonus incentive for high achievers. As part of this role, you’ll complete accurate insurance verifications and prior authorizations to ensure timely reimbursement. You’ll also collaborate with clinicians and insurance payors in a fast‑paced environment to manage claims, documentation, and approvals. If you enjoy problem‑solving, staying organized, and making a meaningful impact—both independently and as part of a team—we’d love to hear from you. Qualifications - 5+ years of prior authorization experience - Required - Proficient in EPIC - Strong communication, problem-solving, and organizational skills. - Detail-oriented, analytical, and able to multitask in a fast-paced environment. - Proficient in TEAMS Requirements - Proficient in Idaho and Washington Payor portals, along with others - Proficient in EPIC for demographic information, patient clinical notes, and referrals - Must be proficient in submission of prior authorization in EPIC - Communicate with providers and insurance companies regarding authorization status. - Maintain HIPAA compliance and support daily revenue cycle operations. Benefits - Dental insurance - Health Insurance - Accrued Paid Time Off - Employee assistance program

• Lead AI solution deployments across a broad range of clients. • Own the end-to-end client engagement for AI deployments. • Lead and manage the cross-functional deployment team responsible for the delivery of AI solutions. • Provide strategic guidance to clients on best practices for deployment. • Oversee the deployment lifecycle from initial sale through successful deployment.

• Lead the design and execution of Baylor Genetics’ enterprise data and AI strategy • Establish a scalable data platform, enabling advanced analytics, and driving AI adoption across the organization • Build and lead a high-performing team while partnering across Technology, Genomic Sciences, Operations, and Commercial teams • Define and implement the enterprise data platform architecture (e.g., Databricks, Snowflake, Azure ecosystem) • Establish a single source of truth across clinical, operational, and financial data • Design scalable data ingestion, processing, and storage frameworks • Ensure interoperability across systems including LIMS, reporting platforms, and enterprise applications • Define and execute the company-wide AI strategy • Identify and prioritize high-value AI use cases (clinical, operational, customer-facing) • Lead development and deployment of AI/ML models and GenAI solutions • Establish governance for responsible AI (compliance, explainability, PHI protection) • Establish enterprise data governance framework (ownership, quality, lineage) • Ensure compliance with HIPAA, CAP, and other regulatory standards • Define data access, security, and retention policies • Partner closely with Security and Compliance teams • Build enterprise analytics capabilities to support decision-making • Standardize reporting and KPI definitions across the organization • Enable self-service analytics for business stakeholders • Build and lead the Data & AI organization (data engineering, data science, analytics) • Establish clear operating model and team structure • Mentor and develop technical and functional leaders • Partner with Product Engineering and Platform teams for execution alignment

• Operate autonomously to design complex evaluation frameworks and provide structured training data. • Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions across travel, finance, and technical support domains. • Evaluating AI model performance across standardized qualitative and quantitative metrics. • Assessing the model's basic computer programming literacy and understanding of structured data within a support context. • Contributing to the development of diverse, high-quality audio datasets that accurately reflect real customer expectations for clarity, efficiency, and natural conversational flow.