Job description
Provide operational and scientific support for global clinical validation and research studies under the guidance of the Director of Clinical & Scientific Affairs. Key responsibilities include supporting protocol development, study reports, scientific conference deliverables (posters and presentations), and regulatory documentation while collaborating cross-functionally to ensure rigorous and timely study execution.

Proactively contributes to clinical study processes by supporting the execution and tracking of clinical operations, ensuring compliance with company SOPs, ICH-GCP guidelines, regulatory requirements, and patient safety standards.

Assists in developing and writing clinical study protocols, study plans, Ethics/IRB submission packages, CRFs, and clinical study reports.

Facilitates training for clinical study teams according to study protocols and maintains strong relationships with investigators and external experts to support effective project delivery.

Supports data quality initiatives and helps implement activities required for timely, high-quality deliverables (e.g., collaborating with statisticians and data managers, participating in data review).

Analyzes, interprets, and presents clinical study results to internal and external stakeholders, including contributing to scientific peer-reviewed publications.

we are looking for a Clinical Data Labeler.
The Clinical Data Labeler contributes significantly to the training of our AI models, which aim to enhance the quality of life for senior citizens. This position involves meticulously tagging and classifying textual data based on clinical and technical criteria to ensure high-quality AI training datasets.
Key Responsibilities:
Review and analyze clinical textual data, including speech and other relevant sounds.
Accurately tag and classify textual data using clinical and technical criteria.
Collaborate with the AI team to refine and improve labeling guidelines.
Communicate regularly with the team to report challenges, suggest improvements, and ensure consistent data labeling.
Maintain a high level of attention to detail to ensure quality and accuracy in data labeling.