Shivani Amin

Clinical research professional with 5+ years of experience leading complex medical device studies while driving improvements in patient recruitment, protocol adherence, and study execution. Proven ability to manage multi-study portfolios (10 concurrent trials), increase enrollment by up to 40%, and remediate high-risk studies to full compliance. Experienced working directly with physicians and cross-functional stakeholders, generating clinical insights that inform product utilization, training strategies, and overall performance.

Combines strong clinical execution with a growing focus on product adoption and real-world impact.

Clinical Research Lead / Study Coordinator – Vascular Medical Devices at [Company Name] (Present)

Led execution and delivery of complex vascular clinical trials while partnering closely with physicians and stakeholders to optimize both study outcomes and real-world device utilization.

• Managed 10 concurrent clinical studies, maintaining 90–100% adherence to timelines, protocol requirements, and regulatory standards.
• Increased patient recruitment by 25–40% and improved retention by aligning enrollment strategies with physician workflows and patient needs.
• Served as primary liaison between surgeons, sponsors, and cross-functional teams, ensuring alignment and accelerating issue resolution by ~30%.
• Led remediation of studies with significant CAPA and quality issues, reducing protocol deviations by 35–50% and restoring audit readiness.
• Strengthened protocol adherence and data quality by implementing structured workflows and improving site-level execution.
• Proactively identified operational risks and implemented mitigation strategies to ensure on-time study delivery across multiple trials.
• Worked directly with physicians in procedural settings, developing deep understanding of device performance, usability, and integration into clinical workflows.
• Captured and communicated voice-of-customer insights (physicians, clinical staff) to internal stakeholders, contributing to improvements in product usage, training needs, and workflow fit.
• Supported clinical training and education efforts, helping improve correct device utilization and consistency across sites.
• Contributed clinical data insights and real-world observations that supported internal discussions around product performance, adoption trends, and optimization opportunities.

Graduate Researcher – Tuberculosis Drug Discovery (Machine Learning) at [University Name]

Developed machine learning models to improve compound screening efficiency and support research decision-making.

• Developed machine learning models (JMP, Jupyter Notebook) improving compound screening efficiency by ~30%.
• Translated complex datasets into clear, actionable insights, supporting research prioritization and decision-making.
• Collaborated across teams to align outputs with clinical and therapeutic relevance.

Research Fellow – Infectious Disease & Immunology (COVID-19 Rapid Response) at National Institutes of Health

Conducted epitope mapping and supported COVID-19 vaccine design as part of a rapid-response research team.

• Conducted epitope mapping to support next-generation COVID-19 vaccine design, identifying high-value immunological targets.
• Selected for a rapid-response team, delivering time-sensitive data that informed public health decisions.
• Accelerated research timelines by 30–50%, enabling faster translation of findings into actionable outcomes.
• Collaborated across multidisciplinary teams to ensure high-quality data and meaningful interpretation for stakeholders.

Master's in Biomedical Sciences – Rutgers University

Bachelor's of Science in Biology – North Carolina State University

Bachelor's of Arts in Chemistry – North Carolina State University