Clinical Pharmacology Intern

Clinical Pharmacology Intern – Oncology

3 Months (12 weeks can NOT be shortened)

San Diego, CA (Hybrid)

Max PR: $29.23/hr→ (please ensure the team is aware this is nonnegotiable) 

**ONE POSITION AND THREE SUB SPOTS**

PLEASE ENSURE ALL SUBS HAVE THE SUBMITTAL ATTACHMENT in their BH profiles at the time of submittal (this is located in the client cheat sheet)→  Resume (no logo or personal contact info), RTR all must be uploaded separately. NO VISA CANDIDATES

MSP Notes:

•       Role title: Clinical Pharmacology Intern – Oncology
•       Duration: 12 weeks (cannot be shortened)
•       Schedule: Monday–Friday, ~8–5, aligned to manager hours
•       Location: Hybrid preferred (San Diego / Torrey Heights or Bothell, WA), remote considered within the US
•       Candidate level: PhD students only (strong preference for 4th–5th year)
Core Focus of the Role
•       Clinical pharmacology & MIDD modeling
•       Projects involve MSM modeling and potential TGI/OS modeling
•       Intern will work with real-world data and collaborate with cross-functional partners

Must-Have Requirements (Non Negotiable)
1.      Strong analytical skills
2.      Hands-on experience using R
o       R is the only true must-have software skill
3.      Demonstrated, practical experience in MIDD
o       Hands-on project work, not just coursework or workshops
Key signal Manager emphasized:
“We don’t want someone who is just enthusiastic about learning MIDD — we need someone who has actually done it.”

Nice-to-Have / Preferred
•       Experience with NLME tools (Non-MEM, Monolix, or similar)
o       Specifically moved from requirement → nice to have
•       PhD students in 4th or 5th year
•       PhD thesis directly involving MIDD
•       Publications, posters, or conference presentations
o       Especially if tied to pharmacometrics or MIDD

Red Flags / Disqualifiers*****
•       Lists technical skills (R, MIDD, modeling) without real hands-on experience
•       Experience limited to:
o       Training courses only
o       Workshops only
o       Passive exposure without execution
•       Cannot clearly explain past modeling projects

Interview / Screening Signals Manager Values
•       Ability to walk through a real project
•       Comfort giving a short (≈5-minute) explanation or presentation of prior MIDD work
•       Clear explanation of:
o       Their specific role
o       Data used
o       Modeling decisions
o       Outcomes

Job Description

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.

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. 

Job 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. 

Education & Qualifications

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)