Director/Senior Director - Program management - Medical Monitoring Implementation Lead/Clinical SME

Function: Implementation Services — Medical Monitoring
Role Type: Client-facing clinical implementation and validation

Role Summary

The Medical Monitoring Implementation Lead serves as the clinical authority for Patient Insights, medical-review dashboards, patient profiles, analytics, and related clinical data-review solutions. This role ensures that implementations are not only technically accurate, but also clinically meaningful, complete, interpretable, and suitable for medical decision-making.

The individual partners with Medical Monitors, Clinical Scientists, Safety Physicians, Data Management, Biostatistics, Product, Engineering, and Implementation teams throughout requirements definition, configuration, clinical validation, user acceptance testing, deployment, and adoption.

Key Responsibilities

      Own the clinical workstream from scoping through production, including protocol interpretation, requirements definition, dashboard design, clinical sign-off, training, and adoption.

      Review protocols, amendments, statistical analysis plans, data-review plans, CRFs, data specifications, coding conventions, and study-specific rules to identify safety, efficacy, dosing, stopping, AESI, and clinical-review requirements.

      Translate study requirements into practical medical-monitoring workflows, dashboards, patient profiles, listings, alerts, checks, filters, visualizations, and review criteria while maintaining traceability to source requirements.

      Define the Medical Monitor and Clinical Scientist personas, including their decisions, review sequence, escalation points, evidence needs, and expectations for patient-level and aggregate data.

      Determine which requirements can use standard product capabilities and which require study-specific configuration, ensuring reusable functionality is not applied where protocol definitions, visit structures, assessment rules, or terminology differ.

      Support integrated patient review across adverse events, serious adverse events, AESIs, CTCAE grading, MedDRA coding, laboratory trends, Hy’s Law indicators, vital signs, ECGs, physical examinations, exposure, dose modifications, concomitant medications, medical history, disposition, deaths, and other relevant clinical domains.

      Evaluate whether timelines, graphs, tables, counts, denominators, units, reference ranges, analysis windows, populations, and filters enable safe clinical interpretation and clearly expose missing, inconsistent, delayed, or conflicting information.

      Support efficacy and disease-assessment workflows, including tumor response, lesion-level review, hematologic or bone-marrow response, longitudinal assessments, swimmer plots, and time-to-event views. For oncology studies, apply relevant response frameworks such as RECIST 1.1, iRECIST, including baseline, nadir, sum-of-diameters, response category, best overall response, duration of response, progression, and post-progression review.

      Reconcile investigator assessments, central reviews, EDC data, safety systems, laboratory systems, imaging sources, and derived outputs. Identify whether discrepancies arise from source data, mappings, transformations, derivations, product logic, configuration, or user interpretation.

      Lead clinical validation and UAT of dashboards, patient profiles, listings, derivations, alerts, and AI-generated outputs. Develop clinically meaningful test scenarios, including negative cases, boundary conditions, missing data, conflicting dates, partial dates, dose interruptions, repeat assessments, unscheduled visits, and other edge cases.

      Document issues with clear clinical impact, root cause, corrective action, retest evidence, and final disposition. Apply risk-based prioritization and do not recommend clinical sign-off when material patient-safety, data-integrity, or interpretability concerns remain unresolved.

      Conduct clinician-to-clinician discovery sessions, workflow walkthroughs, design reviews, office hours, UAT sessions, defect triage, and readiness assessments. Convert medical feedback into precise user stories, acceptance criteria, configuration rules, and engineering requirements.

      Serve as the clinical bridge between users and Product, Engineering, Quality, and Implementation teams. Prioritize defects and enhancements by patient-safety and decision-making impact while balancing usability, performance, scalability, and reuse.

      Identify recurring clinical requirements that can become reusable therapeutic-area packages, medical checks, templates, smart suggestions, standard queries, review workflows, and product capabilities.

      Evaluate AI-generated summaries, narratives, visualizations, recommendations, and suggested actions for clinical relevance, source grounding, traceability, reproducibility, explainability, false positives, false negatives, unsupported conclusions, and hallucination risk. Define appropriate human review and approval controls.

      Develop clinical review guides, validation summaries, issue assessments, decision rationales, patient profiles, safety summaries, and training materials using concise, accurate, and auditable medical-writing practices.

      Train Medical Monitors, Clinical Scientists, study teams, champions, and power users on role-specific workflows, data interpretation, review practices, escalation procedures, and appropriate use of analytics or AI-supported features.

Required Qualifications

      Medical degree such as MD, DO, MBBS, or equivalent clinical qualification.

      At least ten years of experience in pharmaceutical, biotechnology, CRO, clinical research, or clinical technology environments, including substantial direct experience in medical monitoring, clinical development, clinical science, or safety review.

      Demonstrated experience reviewing patient-level safety and efficacy data and identifying clinically meaningful trends, inconsistencies, missing information, and potential safety signals.

      Strong knowledge of AE, SAE, AESI, TEAE, CTCAE, MedDRA, laboratory interpretation, exposure, concomitant medications, dose modifications, study disposition, and patient-level benefit-risk review.

      Ability to interpret protocols, amendments, SAPs, CRFs, data-review plans, TFLs, investigator brochures, safety narratives, clinical summaries, and clinical study reports.

      Experience with medical-review dashboards, patient profiles, listings, data visualizations, or clinical analytics solutions.

      Working knowledge of data from EDC, safety, central laboratory, ECG, imaging, eCOA, coding, and other clinical systems.

      Ability to understand source-to-target mappings, clinical derivations, data transformations, reconciliation, and validation logic. Programming experience is not required, but the individual must communicate clinical logic precisely to technical teams.

      Experience designing and executing clinical UAT, defining acceptance criteria, documenting evidence, and assessing defects according to clinical risk.

      Knowledge of ICH-Google Cloud Platform, GxP expectations, data integrity, auditability, privacy, traceability, and controlled change practices.

      Strong medical-writing, facilitation, stakeholder-management, and clinician-facing communication skills.

      Ability to independently own a clinical implementation workstream from requirements discovery through deployment and adoption.

Strongly Preferred

      Medical-monitoring experience in oncology across solid tumors, hematologic malignancies, or both.

      Practical experience applying RECIST 1.1, iRECIST, RANO, RANO-BM, Choi, or other disease-response criteria.

      Formal medical-writing experience involving safety narratives, clinical study reports, investigator brochures, clinical summaries, or regulatory responses.

      Experience in early-phase trials, dose escalation, real-time safety review, signal detection, or safety-review committee support.

      Familiarity with platforms and standards such as Medidata Rave, Veeva, CDISC/SDTM, clinical analytics platforms, or integrated review solutions.

      Experience evaluating or implementing AI-enabled clinical review, summarization, or decision-support capabilities.

      Prior consulting, implementation-services, or client-facing experience working with senior medical and clinical-development stakeholders.

Core Competencies

Clinical judgment and patient-safety orientation; protocol-to-dashboard translation; safety and efficacy review; therapeutic-area agility; clinical data interpretation and reconciliation; medical-monitoring workflow design; clinical validation and risk assessment; medical and regulatory writing; clinician-to-engineer translation; root-cause analysis; stakeholder trust; and end-to-end implementation ownership.