Clinical Pharmacology Intern – Oncology
Aequor JD ·www.aequor.com
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Clinical Pharmacology Intern – Oncology
Duration: 3 months (12 weeks)
Internship Overview
We are seeking a highly motivated graduate-level intern to support quantitative modeling analyses in early oncology drug development. This internship focuses on applying advanced modeling approaches to leverage early-phase clinical data for predicting long-term efficacy outcomes such as progression-free survival (PFS) and overall survival (OS). The intern will work closely with experienced clinical pharmacologists and quantitative scientists to support data-driven oncology development decisions.
Project Description
Early-phase oncology trials often rely on short-term endpoints (e.g., objective response rate or longitudinal tumor burden) that may not fully capture long-term clinical benefit. Quantitative modeling methods such as Tumor Growth Inhibition–Overall Survival (TGI–OS) models and multistate models (MSMs) can help bridge this gap by linking early tumor or disease dynamics to survival outcomes.
During this internship, the selected candidate will apply TGI–OS and/or MSM-based modeling approaches to selected oncology datasets, including early-phase clinical trial data and, where appropriate, real-world data (RWD). The project will emphasize interpretation of early efficacy signals and their relevance to oncology development decision-making.
Key Responsibilities
Conduct a targeted literature review on quantitative modeling approaches used to predict long-term oncology efficacy endpoints (PFS and OS).
Analyze early-phase oncology clinical trial data using appropriate quantitative modeling frameworks.
Implement TGI–OS and/or multistate modeling approaches to characterize tumor dynamics, disease state transitions, and survival outcomes.
Explore relationships between short-term efficacy endpoints (e.g., tumor size dynamics) and long-term clinical benefit.
Interpret and synthesize results in the context of oncology drug development and regulatory decision-making.
Prepare a final presentation and written summary for internal scientific stakeholders.
Required Qualifications
Enrollment in a graduate program (MS, PhD, PharmD, or equivalent) in pharmacometrics, clinical pharmacology, biostatistics, biomedical engineering, quantitative sciences, or a related discipline
Strong quantitative and analytical background with interest in oncology drug development
Experience with statistical or data analysis programming in R
Familiarity with longitudinal data analysis, survival analysis, or applied statistical modeling
Ability to clearly communicate scientific concepts, results, and interpretations in both written and verbal formats
Preferred Qualifications
Prior exposure to pharmacometric or disease progression modeling (e.g., TGI, survival models, multistate models)
Familiarity with early-phase oncology clinical trial data and endpoints (e.g., ORR, tumor burden, PFS, OS)
Interest in model-informed drug development (MIDD) and translational oncology research
Experience working with real-world data (RWD) or observational datasets
Learning Outcomes
Experience using NONMEM (or other non-linear mixed effects modeling software)
By the end of the 3 month internship, the intern will have gained hands-on experience applying quantitative oncology modeling approaches, interpreting early efficacy data in relation to long-term outcomes, and communicating results to multidisciplinary clinical development teams.
Duration: 3 months (12 weeks)
Internship Overview
We are seeking a highly motivated graduate-level intern to support quantitative modeling analyses in early oncology drug development. This internship focuses on applying advanced modeling approaches to leverage early-phase clinical data for predicting long-term efficacy outcomes such as progression-free survival (PFS) and overall survival (OS). The intern will work closely with experienced clinical pharmacologists and quantitative scientists to support data-driven oncology development decisions.
Project Description
Early-phase oncology trials often rely on short-term endpoints (e.g., objective response rate or longitudinal tumor burden) that may not fully capture long-term clinical benefit. Quantitative modeling methods such as Tumor Growth Inhibition–Overall Survival (TGI–OS) models and multistate models (MSMs) can help bridge this gap by linking early tumor or disease dynamics to survival outcomes.
During this internship, the selected candidate will apply TGI–OS and/or MSM-based modeling approaches to selected oncology datasets, including early-phase clinical trial data and, where appropriate, real-world data (RWD). The project will emphasize interpretation of early efficacy signals and their relevance to oncology development decision-making.
Key Responsibilities
Conduct a targeted literature review on quantitative modeling approaches used to predict long-term oncology efficacy endpoints (PFS and OS).
Analyze early-phase oncology clinical trial data using appropriate quantitative modeling frameworks.
Implement TGI–OS and/or multistate modeling approaches to characterize tumor dynamics, disease state transitions, and survival outcomes.
Explore relationships between short-term efficacy endpoints (e.g., tumor size dynamics) and long-term clinical benefit.
Interpret and synthesize results in the context of oncology drug development and regulatory decision-making.
Prepare a final presentation and written summary for internal scientific stakeholders.
Required Qualifications
Enrollment in a graduate program (MS, PhD, PharmD, or equivalent) in pharmacometrics, clinical pharmacology, biostatistics, biomedical engineering, quantitative sciences, or a related discipline
Strong quantitative and analytical background with interest in oncology drug development
Experience with statistical or data analysis programming in R
Familiarity with longitudinal data analysis, survival analysis, or applied statistical modeling
Ability to clearly communicate scientific concepts, results, and interpretations in both written and verbal formats
Preferred Qualifications
Prior exposure to pharmacometric or disease progression modeling (e.g., TGI, survival models, multistate models)
Familiarity with early-phase oncology clinical trial data and endpoints (e.g., ORR, tumor burden, PFS, OS)
Interest in model-informed drug development (MIDD) and translational oncology research
Experience working with real-world data (RWD) or observational datasets
Learning Outcomes
Experience using NONMEM (or other non-linear mixed effects modeling software)
By the end of the 3 month internship, the intern will have gained hands-on experience applying quantitative oncology modeling approaches, interpreting early efficacy data in relation to long-term outcomes, and communicating results to multidisciplinary clinical development teams.
Frequently asked questions
Who is hiring for the Clinical Pharmacology Intern – Oncology role?
Aequor JD is hiring for the Clinical Pharmacology Intern – Oncology position, a Shazamme client. Apply directly on the employer's career site.
Where is the Clinical Pharmacology Intern – Oncology job located?
The Clinical Pharmacology Intern – Oncology role with Aequor JD is based in San Diego, CA, US.
What does the Clinical Pharmacology Intern – Oncology role pay?
Aequor JD lists the Clinical Pharmacology Intern – Oncology role at USD 30–32 per hour.
Is the Clinical Pharmacology Intern – Oncology role full-time or contract?
This is a full time position at Aequor JD.
What experience level is the Clinical Pharmacology Intern – Oncology role?
The Clinical Pharmacology Intern – Oncology position is aimed at intern-level candidates.
How do I apply for the Clinical Pharmacology Intern – Oncology role at Aequor JD?
Apply directly on Aequor JD's career page via the Apply button on this listing. ZammeJobs links straight through to the employer's ATS — no third-party form, no resume database.