QuantHealth is at the forefront of developing an AI-powered platform for clinical trial simulation and optimization. Our mission is to provide innovative solutions to pharmaceutical and biotechnology companies to de-risk the drug and clinical development process. We are looking for a detail-oriented Project Manager to drive operational excellence and support our data science teams in delivering innovative solutions within the clinical field.
Key Responsibilities:
- R&D Planning: Assist in developing and implementing operational strategies to enhance efficiency and productivity within the R&D department. Routinely collect requirements from R&D teams to aid in development prioritization and the creation of quarterly work plans.
- Resource Management: Help oversee resource allocation, including personnel and third-part services, to ensure optimal utilization. Support resource planning by tracking team availability and project requirements.
- Project Oversight: Help with high-level management of data science projects, ensuring they meet quality standards and deadlines.
- Process Optimization: Identify bottlenecks and implement process improvements to enhance scalability and automation.
- Cross-functional Collaboration: Work closely with data scientists, scientists, and engineers to help align R&D activities with the teams’ and company’s objectives. Organize meetings, workshops, and team events.
- Reporting and Analytics: help developing tools, metrics and KPIs to monitor performance and report on R&D project progress and operational efficiency.
- Risk Management: set up tools to Identify, monitor, and mitigate potential risks in R&D projects.
- Documentation Management: Maintain project documentation, including reports, plans, policies and standard operating procedures.
Qualifications:
- Bachelor's or Master's degree in a relevant field (e.g., Industrial engineering and management, Data Science, Biomedical Engineering).
- 3-4 years of experience in operational roles, preferably within technical and business-oriented environments.
- Good understanding of machine learning and data science workflows in the clinical domain is a plus.
- Proficiency with collaboration and project management tools (e.g., Slack, Asana, Confluence).
- Strong organizational and multitasking skills.
- Excellent verbal and written communication abilities.
- High attention to detail and a proactive approach to problem-solving.
