Job DescriptionAssociate Clinical Data Specialist
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- Cross-Functional Collaboration: Partner with engineering, product management, and quality teams to assess project progress and ensure alignment with medical image annotation needs, quality goals, and standards.n
- Implement and evaluate frameworks to ensure reliability, scalability, and maintainability across the AI/ML model development lifecycle.n
- Oversee the integration and execution of annotation workflows, and improving processes to enhance efficiency and system reliability.n
- Develop, Optimizing Tools & Processes: Lead the evaluation and optimization of annotation tools and development processes, ensuring teams use the most effective solutions for code analysis, automation, and collaboration.n
- Regulatory Compliance & Patient Safety: Ensure annotation output meets regulatory requirements and patient safety standards and Quality Improvement Plans.n
- Leveraging Reusable Solutions: Promote the reuse of software components and frameworks across teams,



enhancing efficiency and consistency while reducing development time.n
- Continuous Improvement: Analyze performance data to identify areas for improvement, leading initiatives to enhance data annotation quality, safety, and user experience. Implement process improvements through regular assessments and industry benchmarking.n
- Stakeholder Communication: Regularly update stakeholders on progress, annotation jobs, and quality, providing strategic recommendations for process improvements and alignment with business objectives.n

nMust Have
nMinimum Qualification
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- B-Tech/BE in Computer Science, Electronics, or related field with 5+ years of experience in Clinical Data/Image Annotationn

nRequired Expertise: The ideal candidate would have experience in the following areas:
nRequired Skills
nBiomedical Signal Interpretation
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- Ability to review waveform visualizations and identify signal patterns.n
- Comfortable working with ti

📌 Associate Clinical Data Specialist (Hyderabad)
🏢 HealthMinds Consulting
📍 Hyderabad