
1001 - 5000 employees
Founded 1988
☁️ SaaS
🤝 B2B
🔬 Science
SaaS • B2B • Science
Clinisys is a global provider of laboratory informatics and diagnostics software, offering laboratory information management systems (LIS/LIMS), cloud-based laboratory platforms, and related professional services to laboratories across healthcare, life sciences, environmental, food & beverage, and other sectors. With over 40 years of experience and more than 3,000 customers in 34 countries, Clinisys delivers solutions that support sample traceability, regulatory compliance, population-scale testing, and integration of emerging technologies such as AI. The company combines local market knowledge and global scale, backed by product development, support, and deployment teams to help organisations run efficient, compliant laboratory operations.
🕒 April 23
🗣️🇩🇪 German Required
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1001 - 5000 employees
Founded 1988
☁️ SaaS
🤝 B2B
🔬 Science
SaaS • B2B • Science
Clinisys is a global provider of laboratory informatics and diagnostics software, offering laboratory information management systems (LIS/LIMS), cloud-based laboratory platforms, and related professional services to laboratories across healthcare, life sciences, environmental, food & beverage, and other sectors. With over 40 years of experience and more than 3,000 customers in 34 countries, Clinisys delivers solutions that support sample traceability, regulatory compliance, population-scale testing, and integration of emerging technologies such as AI. The company combines local market knowledge and global scale, backed by product development, support, and deployment teams to help organisations run efficient, compliant laboratory operations.
• Leverage AI-enabled project management and analytics tools to support planning, forecasting, and decision-making. • Use data-driven insights (predictive indicators, trend analysis) to anticipate project risks, delays, or resource constraints and proactively define mitigation actions. • Oversee projects involving AI-enabled or data-driven healthcare solutions, ensuring alignment with scope, regulatory constraints, and customer expectations. • Ensure that AI-related components (where applicable) are properly considered in project scope, risk management, and quality assurance activities. • Facilitate alignment between services, product, technical, and data teams when projects involve advanced analytics or AI-assisted functionalities. • Support stakeholders’ understanding of AI-enabled project outcomes, including benefits, limitations, dependencies, and operational impacts. • Identify opportunities to improve project execution through automation, intelligent reporting, or AI-assisted monitoring of project performance.
• Bachelor’s degree in Project Management, Business Administration, or a related field. • PMP or equivalent certification is preferred. • Minimum of 3–5 years of experience in project management within the services sector. • Knowledge of the Healthcare Sector. • Strong project management skills, including planning, execution, and risk management. • Excellent communication and interpersonal skills. • Ability to manage multiple projects simultaneously. • Proficiency in project management software (e.g., Microsoft Project, Asana, Trello). • Strong problem-solving skills and attention to detail. • Ability to work effectively in a fast-paced and dynamic environment. • Solid understanding of AI and data-driven concepts and how they are applied in healthcare, laboratory informatics, and service delivery. • Ability to use AI-assisted project management tools (e.g. predictive scheduling, risk scoring, automated reporting) to improve planning accuracy and execution. • Awareness of data quality, data governance, and compliance requirements when managing projects involving AI or advanced analytics. • Capability to assess and manage AI-related project risks, including dependency on data availability, model limitations, explainability, and regulatory constraints. • Familiarity with AI-supported decision-making, performance dashboards, and intelligent reporting tools. • Understanding of ethical and regulatory considerations related to AI in healthcare environments (e.g. transparency, traceability, patient data protection). • Ability to translate complex AI-related topics into clear, business-oriented communication for customers and non-technical stakeholders.
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