Neuroscience Quality Assurance Lead

Job Summary

The Neuroscience Quality Assurance Lead oversees, evaluates, and optimizes the quality of neurobiological and cognitive science training data and contributor performance across specialized data operations. Core daily tasks include performing rigorous spot-checks on AI-generated research summaries and experimental logs, auditing contributor evaluations against complex rubrics, drafting highly detailed written scientific feedback, leading remote onboarding webinars, managing communication workflows over Discord, and eliminating pseudoscientific or clinically overclaimed data from the pipeline.

Required Academic Foundations & Scientific Literacy

• Advanced Academic Track: Bachelor’s, Master’s, PhD, MD/PhD, or equivalent deep professional software/laboratory research experience in Neuroscience, Cognitive Science, Psychology, Neurobiology, Cognitive Psychology, Computational Neuroscience, Neurology-adjacent research, Biology, or Biomedical Sciences.
• Domain Tenure: 3+ years of professional experience operating directly within neuroscience/cognitive science research pipelines, academic teaching, laboratory operations, peer review systems, science communication, experimental design, or advanced scientific data analysis.
• Language Mastery: Advanced professional command of the English language, characterized by the ability to parse complex academic evaluation rubrics and write clear, nuanced technical feedback logs.
• Deep Scientific Comprehension: Comprehensive, working understanding of neural systems, cognitive frameworks, perception mechanics, attentional variables, memory architectures, language processing, decision-making dynamics, neuroanatomy, neural signaling pathways, research methods, and brain-behavior relationships.
• Analytical Data Triage: Sharp technical ability to parse scientific writing against detailed rubrics, instantly isolating neuromyths, overconfident claims, unsupported causal conclusions, flawed study interpretations, incorrect anatomical terminology, pseudoscience, or misleading clinical implications.

Preferred Technical Assets & Operations Tooling

• Methodology Familiarity: Practical exposure to cognitive neuroimaging, behavioral, or statistical methods, including EEG, fMRI, behavioral experiments, computational modeling, neuropsychological assessments, advanced statistics, Python/R/MATLAB scripting, cognitive tasks, or systematic literature reviews.
• Team Leadership & Coordination: Prior experience leading, mentoring, or auditing remote work groups consisting of researchers, reviewers, educators, annotators, science writers, or quality control technicians.
• System Operations Fluency: Comfort operating day-to-day across Discord, Google Workspace (Sheets, Docs, Trackers), project dashboards, and remote project management systems.
• AI Industry Experience: Prior exposure to AI training ecosystems, data annotation platforms, prompt engineering evaluation, or strict rubric-based peer code/text reviews is highly valued.
• Administrative Design: Highly organized approach to building, tracking, and upgrading style guides, FAQs, onboarding portals, calibration tasks, and system honeypots.

Key Responsibilities

1. High-Precision Scientific Review & Rubric Enforcement (40%)

• Perform Systematic Spot-Checks: Conduct rigorous quality audits on completed neuroscience and cognitive science items, assessing datasets for scientific accuracy, conceptual precision, and formatting compliance.
• Critically Assess Complex Datasets: Review AI-generated explanations, research summaries, experimental interpretations, brain-behavior claims, cognitive theory applications, and step-by-step logic sheets.
• Enforce Safety & Ethical Guardrails: Identify, flag, and eliminate pseudoscientific, overconfident, clinically misleading, ethically problematic, or unsupported claims regarding mental health, brain functionality, or cognitive disorders.
• Provide Written Feedback Loops: Deliver precise, constructive, and highly descriptive written feedback to contributors via direct messages, explaining the why behind scientific corrections to permanently elevate platform performance standards.

2. Team Communications, Discord Leadership & Contributor Operations (30%)

• Orchestrate Discord Communication: Lead the operational communication stream on Discord, keeping technical trainers and reviewers updated on changing guidelines, workflow changes, and quality expectations.
• Resolve Scientific Inquiries: Serve as the primary technical point of contact, resolving tricky developer and researcher debates centered around neural mechanisms, experimental design parameters, statistical interpretations, and rubric edge cases.
• Execute Contributor Activation: Monitor contributor metrics, actively messaging inactive or struggling experts to encourage platform activation, resolve workflow blocks, and audit active platform capacity.
• Conduct Calibration Training: Host dynamic remote onboarding calls and technical walk-throughs to quickly bring incoming neuroscience contributors up to speed.

3. Administrative Architecture & Training Resource Development (20%)

• Design Strategic Documentation: Formulate, structure, and maintain internal neuroscience engineering style guides, coding/writing standards, FAQs, and benchmark reference examples.
• Build Calibration Honeypots: Engineer specialized "honeypots" and mock calibration scripts featuring hidden scientific errors or logical flaws to evaluate reviewer sharpness.
• Manage Quality Tracking Infrastructure: Build and maintain detailed Google Sheets tracking systems to monitor team performance, error trends, and data pipeline throughput.

4. Workflow Strategy & QA Optimization (10%)

• Identify Quality Gaps: Analyze historical error data to spot recurring weak links within the scientific data generation pipeline, designing strategic updates to permanently eliminate systemic mistakes.
• Protect Data Privacy: Enforce strict corporate security procedures, ensuring proprietary project guidelines, framework layers, and client model definitions remain entirely confidential.

Core Competencies & Skills

• Rigorous Scientific Skepticism: An unyielding dedication to evidence-based reporting, refusing to compromise on statistical caution, experimental control variables, or academic integrity.
• Meticulous Analytical Oversight: An exceptional eye for qualitative and quantitative errors, capable of spotting hidden logical leaps or overclaimed findings within long data arrays.
• Clear Technical Mentorship: The natural ability to deconstruct complex neurobiological mechanisms down into clear, encouraging, and highly instructional contributor guidance.
• Distributed Workflow Agility: Strong organizational skills, enabling you to manage large remote communication streams across multiple global timezones seamlessly.

Skills Required:

• Health / Medical