Clinical Data Abstraction Specialist - Oncology AI

You will be part of the Oncology Clinical Team at Triomics, focusing on training, validating, and continuously improving AI-driven oncology data extraction and trial-matching systems. Your role involves collaborating closely with NLP, product, and operations teams to refine model outputs, build oncology data dictionaries, optimize abstraction logic, and ensure clinical accuracy across oncology workflows. Key Responsibilities: - Review oncology patient cases for clinical trial eligibility using inclusion/exclusion criteria by validating and refining AI-assisted eligibility determinations. - Provide structured feedback to improve AI model performance on inclusion/exclusion logic. - Translate complex protocol requirements into structured, model-readable logic to enhance automated trial build and screening process. - Review and refine AI-extracted diagnostic, pathology, imaging, and treatment data for accuracy and protocol compliance. - Collaborate with NLP and product teams to tune oncology-specific data dictionaries and abstraction frameworks. - Define and improve abstraction rules to enhance automated clinical data structuring. - Serve as an oncology subject-matter expert to guide model optimization across solid and hematologic malignancies. - Identify systematic error patterns in AI outputs and propose workflow or model-level improvements. - Conduct second-level and quality-control reviews to benchmark AI performance. - Contribute to SOP development for AI-assisted abstraction workflows. Qualifications: - 3+ years of experience in oncology clinical data abstraction, clinical research, or patient-treatment data analysis. - Strong oncology domain expertise across diagnosis, staging, grading, and systemic therapies. - Experience reviewing medical records for oncology clinical trials. - Comfort working with AI-assisted workflows and structured data systems. - Certification in Clinical Research or Clinical Data Management preferred. Skills and Abilities: - Excellent written and verbal communication skills. - Sound judgment in abstracting medical data. - High adaptability to changing assignments. - Proficient with Microsoft Office (Word, Excel, Outlook, PowerPoint). Triomics is revolutionizing the oncology industry with its AI platform, providing the opportunity to impact and benefit patients worldwide. The company offers company-sponsored workations, challenging problems in a highly regulated industry, and the chance to work with experts from multiple industries. Additionally, you will receive best-in-industry compensation. This role requires strong expertise in histological classification of solid malignancies, hematologic cancers, biomarkers/tumor markers, TNM classification, types of cancer therapy, and the mechanism of action of chemotherapy. You will directly affect AI workflows that accelerate cancer research and improve patient outcomes globally. The culture at Triomics is fast-paced, ownership-driven, and offers comprehensive health insurance, flexible working hours, and other benefits. You will be part of the Oncology Clinical Team at Triomics, focusing on training, validating, and continuously improving AI-driven oncology data extraction and trial-matching systems. Your role involves collaborating closely with NLP, product, and operations teams to refine model outputs, build oncology data dictionaries, optimize abstraction logic, and ensure clinical accuracy across oncology workflows. Key Responsibilities: - Review oncology patient cases for clinical trial eligibility using inclusion/exclusion criteria by validating and refining AI-assisted eligibility determinations. - Provide structured feedback to improve AI model performance on inclusion/exclusion logic. - Translate complex protocol requirements into structured, model-readable logic to enhance automated trial build and screening process. - Review and refine AI-extracted diagnostic, pathology, imaging, and treatment data for accuracy and protocol compliance. - Collaborate with NLP and product teams to tune oncology-specific data dictionaries and abstraction frameworks. - Define and improve abstraction rules to enhance automated clinical data structuring. - Serve as an oncology subject-matter expert to guide model optimization across solid and hematologic malignancies. - Identify systematic error patterns in AI outputs and propose workflow or model-level improvements. - Conduct second-level and quality-control reviews to benchmark AI performance. - Contribute to SOP development for AI-assisted abstraction workflows. Qualifications: - 3+ years of experience in oncology clinical data abstraction, clinical research, or patient-treatment data analysis. - Strong oncology domain expertise across diagnosis, staging, grading, and systemic therapies. - Experience reviewing medical records for oncology clinical trials. - Comfort working with AI-assisted workflows and structured data systems. - Certification in Clinical Research or Cl