Director of Quality Control (QC)

Overview

NeuroDiscovery AI is an AI-native company leveraging our proprietary NeuroLLM` technology trained on millions of neurology patient records to transform clinical research and patient care. We partner with leading neurology provider practices across the United States to accelerate clinical trial identification, generate real-world evidence (RWE), and translate cutting-edge scientific advances into improved patient outcomes.

As we scale our platform across petabyte-scale multimodal healthcare data, AI models, and regulated evidence generation, we are seeking a Director of Quality Control (QC) to build and lead a comprehensive quality function that ensures trust, reproducibility, and regulatory readiness across everything we ship.

Role Overview

The Director of Quality Control will architect and lead NeuroDiscovery`s end-to-end quality control strategy across data pipelines, AI,ML systems, real-world evidence workflows, and client-facing scientific outputs. This role owns data integrity, model reliability, scientific rigor, and compliance readiness, ensuring that NeuroLLM` outputs and RWE deliverables meet the highest clinical, regulatory, and partner expectations.

This is a hands-on leadership role reporting to the Chief Scientific Officer, CEO, responsible for building QC frameworks, tooling, and teams that scale with the company and withstand regulatory and client scrutiny.

Key Responsibilities

• Strategic Leadership
• Define and execute NeuroDiscovery`s 3 year Quality Control roadmap aligned with AI, RWE, and clinical research business goals
• Partner closely with Research, AI,ML, Data Engineering, Product and Clinical research teams to embed quality gates across product and research lifecycles
• Build, hire, and mentor a high-performing QC organization (quality engineers, validation specialists, scientific reviewers)
• Establish company wide quality standards, SOPs, and governance models for data, models, scientific outputs and escalation mechanisms
• Quality Architecture & Frameworks
• Design scalable QC frameworks across
• Multimodal healthcare data (EHR, imaging, genomics, claims, biosignals)
• Machine learning models (training, validation, deployment, monitoring)
• Real-world evidence (RWE) analytics and scientific outputs
• Define automated quality checks, validation rules, acceptance thresholds, and release gates
• Build observability-driven quality systems with lineage, versioning, reproducibility, traceability and auditability
• Data Quality Control
• Own QC for clinical data ingestion, normalization, and transformation across multiple provider systems
• Ensure data completeness, consistency, accuracy, timeliness, and provenance
• Define and enforce data validation pipelines, anomaly detection, and quality scoring
• Partner with Data Engineering to implement schema validation, freshness checks, and lineage tracking
• Model Quality & AI Governance
• Define model validation and evaluation protocols including performance benchmarking, bias detection, safety checks, and robustness testing
• Establish model drift monitoring and post-deployment quality surveillance
• Ensure alignment between model performance metrics and clinical relevance
• Partner with AI,ML teams to govern training data quality, evaluation datasets, and inference pipelines
• QC for advanced methodologies like Knowledge Graphs, Topological Data Analysis, and platform wide outputs
• Contribute to internal AI governance frameworks for healthcare and clinical AI systems
• Real-World Evidence & Scientific Quality
• Establish QC standards for RWE generation, statistical analyses, and evidence packages
• Ensure reproducibility, traceability, and scientific defensibility of all evidence delivered to partners
• Review and approve client-facing analytics, reports, and scientific outputs
• Act as a quality authority for methodology, assumptions, and limitations
• Compliance, Documentation, Audit & Regulatory Readiness
• Ensure QC frameworks align with FDA RWE guidance, healthcare AI expectations, HIPAA, GDPR, and applicable global regulations
• Maintain audit-ready documentation, SOPs, validation reports, and quality logs
• Serve as the quality point of contact for client audits, partner reviews, and regulatory inquiries
• Proactively identify quality and compliance risks and drive corrective and preventive actions
• Client & External Engagement
• Act as a quality ambassador in discussions with pharma, biotech, and research partners
• Support pre-sales and delivery conversations where quality, validation, and compliance credibility are critical
• Translate complex quality and validation concepts into clear, partner-facing narratives
• Execution Excellence
• Embed QC checkpoints into CI,CD, MLOps, and data pipeline workflows
• Lead root-cause analysis for quality incidents and drive systemic fixes
• Define and track quality KPIs, dashboards, and risk indicators for leadership visibility
• Balance speed and rigor in a fast-moving AI-native environment
  

Required Qualifications

• 12+ years of experience in quality control, data governance, QA,QC, scientific validation, or regulated analytics
• Proven experience building quality frameworks for complex data and ML systems
• Strong understanding of healthcare data, clinical research workflows, and evidence generation
• Experience working cross-functionally with engineering, AI,ML, product, and clinical teams
• Demonstrated ability to scale quality systems in high-growth, high-complexity environments
  
  

Technical Must Haves

• Data Quality automated validation, anomaly detection, schema enforcement, quality scoring
• Model QC ML evaluation, bias,fairness checks, performance and drift monitoring
• Observability & Auditability lineage, versioning, reproducibility, traceability
• Compliance HIPAA,PHI handling, audit trails, regulated documentation
• Tooling familiarity data quality frameworks (e.g., Great Expectations), monitoring and validation pipelines
  
  

Preferred Experience

• Experience in real-world evidence (RWE), clinical research, or medical AI
• Familiarity with neurology-specific AI applications
• Exposure to multimodal healthcare data (EHR, imaging, genomics, time-series signals)
• Direct involvement with FDA-aligned RWE or healthcare AI validation efforts
• Background in regulated software or scientific platforms
• Experience building QC or validation functions from the ground up
  
  

What You`ll Impact

• Trust in NeuroLLM` and the NeuroDiscovery AI Platform Ensure AI outputs meet clinical and regulatory standards
• RWE credibility Deliver audit-ready, regulator-grade evidence to pharma partners
• Platform reliability Prevent quality failures before they reach production
• Scalable growth Enable expansion across providers and partners without quality erosion
• Patient outcomes Ensure insights driving care and trials are accurate and defensible
  
  

What We Offer

• Competitive compensation
• Equity Ownership in a rapidly growing healthcare AI company
• Impact Define quality standards at the intersection of AI, healthcare, and science
• Team Work with PhD neuroscientists, clinicians, and AI leaders
• Growth Clear path to VP Quality , Head of Quality & Compliance as we scale