Quality Specialist

Our Company

Abyss Solutions is reinventing industrial asset management both above and underwater for step-change improvements in; asset life maintenance cost and reduction of failure risk. Think oil rigs dam walls ship hulls any asset subject to deterioration in water and weather.

We do so utilizing extremely sophisticated unique (and patented) Artificial Intelligence/ Machine Learning software together with a deep understanding of our customers asset integrity needs and processes. Creating a 3D digital twin of customer assets. Using drones and Abyss analytics is just the beginning Abyss comprehensively classifies the state of the asset to identify exactly what maintenance is required providing precise direction.

Purpose & Scope

The Quality Specialist owns and drives quality across live data processing and operational environments acting as the central authority for the organizations Quality Management System (QMS). This role is responsible for implementing and continuously improving internationally recognized quality and management standards i.e. ISO 9001 ISO 42001 and other applicable standards embedding risk-based controls and driving Total Quality Management (TQM) practices to ensure consistent delivery quality and operational efficiency. Working closely with Operations Data Engineering Product and other cross-functional stakeholders the Quality Specialist ensures the accuracy consistency and reliability of data-driven asset intelligence while reducing defects and rework strengthening operational efficiency and driving a culture of accountability continuous improvement and quality ownership.

Tasks

Quality Systems Standards & Compliance

• Implement maintain and continuously evolve the Quality Management System (QMS) aligned quality standards.
• Ensure quality standards are embedded across live data processing and operational workflows.
• Develop maintain and govern quality guidelines quality frameworks and quality document control systems.
• Apply risk-based thinking to adjust controls sampling and inspection rigor based on performance trends.

Operational Quality & Incident Management

• Monitor live quality across point cloud processing data registration outputs and operational workflows while addressing deviations proactively before they impact deliverables.
• Own and execute NCR incident reporting and CAPA cycles end-to-end.
• Conduct structured Root Cause Analysis (RCA) using methodologies such as 5 Whys and Fishbone.
• Identify systemic causes of defects rework and process failures and drive sustainable corrective actions.
• Collaborate with technical and operational teams to identify recurring quality issues implement preventive controls and improve systemic quality.
• Demonstrate empathy and a proactive mindset when engaging with teams during quality issues.

Quality Frameworks & TQM

• Execute wider quality initiatives aligned with enterprise and product strategy.
• Drive Total Quality Management (TQM) principles across data capture processing and delivery teams.
• Lead quality improvement initiatives to reduce defects rework and Cost of Poor Quality (COPQ).
• Promote a culture where quality is treated as a shared responsibility along the process not simply a final-stage inspection activity.

Training Capability Building & Team Support

• Nurture mentor and support the QA team strengthening quality ownership at all levels.
• Develop and deliver practical quality-related training including ISO awareness RCA CAPA and process adherence.
• Build awareness and organizational capability in quality thinking risk awareness and preventive controls.

Process Improvement & Reporting

• Analyze quality data KPIs and trends to translate data into actionable improvement initiatives.
• Develop quality dashboards reports and performance metrics for management review.
• Lead quality improvement projects and initiatives to enhance efficiency and reduce defects.

Knowledge Requirements

• QMS implementation and ISO 9001:2015
• Quality documentation and document control
• NCR incident management CAPA and RCA methodologies
• Total Quality Management (TQM) and continuous improvement methodologies
• Risk-based thinking in operational and data-driven environments
• Exposure to point clouds AI/ML digital twins or visual data processing is highly desirable

Skills & Competencies

• Strong quality ownership proactive and attention to detail mindset with enterprise-level accountability
• Expertise in live operations data processing quality assurance or risk management
• Excellent RCA CAPA execution and incident learning facilitation
• Ability to translate and conduct quality requirements into practical operational controls
• High empathy proactive problem-solving and stakeholder influence
• Strong cross-functional communication and collaboration skills
• Analytical data-driven decision-making capability and problem-solving approach

Qualifications & Experience

• Bachelors degree in Engineering Quality Management AI/ML data science or a related discipline
• 4-8 years of experience in quality management quality assurance or operational quality within live operations engineering technology or data processing environments.
• Proven experience implementing and operating ISO certified QMS standards
• Experience conducting NCR/CAPA management RCA and quality improvement initiatives
• Exposure to point clouds AI/ML digital twins or visual data processing will be an advantage.

Required Experience:

Manager