Associate Director, Quantitative Systems Pharmacology – Oncology

Job Description

We are seeking an experienced talented scientist to join the Quantitative Pharmacology and Pharmacometrics – Immune/Oncology (QP2-IO) team in the role of Associate Director, Quantitative Systems Pharmacology (QSP). QP2-IO team is part of the Global Clinical Development organization and has oversight over drug development aspects related to clinical pharmacology, mechanistic PK-PD modeling and pharmacometrics of oncology drugs from post-PCC to registration. As a subject matter expert, the Associate Director within the QP2-IO Group will work closely with teams of highly collaborative, cross-functional scientists to contribute to the development of novel therapeutics. They develop strategies for quantitative analyses (and commensurate experiments/trials) within and across development programs and/or functions. Associate Directors demonstrate outstanding leadership and communication skills. They collaborate within QP2-IO and with other functional areas, as well as with external vendors and partners if work is outsourced, creating an aligned, quantitative framework to impact strategies and decisions of drug development teams.

Primary Responsibilities:

• Serving as a QSP expert for QP2 -IO for developing and executing model-based analyses including QSP models and mechanistic PK/PD models into programs thereby strengthening our quantitative capabilities on a continuous basis in decision making and driving pipeline impact.

• Assisting asset development teams in bringing forward combination drug approaches by leveraging QSP models to design and prioritize combination clinical trials

• Broad understanding of population pharmacokinetic models and exposure-response models for application in clinical development

• Work in close collaboration with biologists, clinicians, clinical pharmacologists, pharmacometricians and other line functions to improve our understanding of disease mechanisms and modalities

• Framing critical questions and strategy for optimizing model-based analyses on programs

• Maintaining a comprehensive understanding of global regulatory expectations for small molecules and biologics (monoclonal antibodies, antibody drug conjugates, T-cell engagers, bispecifics) in Oncology, authoring regulatory documents (investigational new drugs/INDs, clinical study reports (CSRs), clinical trial applications (CTDs)), and representing QP2-IO at regulatory meetings

Minimum education required:

• Ph.D., with at least four years of pharmaceutical drug development experience relating to: systems biology/pharmacology, mechanistic PKPD, pharmacometrics, mathematics, chemical/biomedical engineering or related field

• Masters or PharmD, with at least six years of experience, where “experience” means having a record of increasing responsibility and independence in a similar role in pharmaceutical drug development

Required Experience:

• Experience in developing mechanistic PK-PD models and QSP models for decision making in early and/or late-stage clinical development

• Experience with mechanistic modeling for antibody drug conjugates (ADCs), bispecifics, and/ or T-cell engagers will be preferred

• Skills in experimental design, mathematical problem solving, critical data analysis/interpretation, and statistics

• Experience with modeling and simulation packages or programming languages (e.g., MATLAB/SimBiology, QSP Designer, R)

• Hands on experience building, calibrating, and validating QSP models

• Familiarity with R NONMEM, WINNONLIN or other similar programing language

• Professional working proficiency in written and verbal communication.

• Experience in IND, BLA and other submissions to global regulatory agencies.

• An exemplary record of increasing responsibility, independence, and demonstrated impact in driving drug development decisions through application of model-based approaches

The remote position is only applicable for those that are not within commutable distance to primary sites noted in the posting. If commutable, the position will be hybrid. Please note that standard commute is (less than) <50 miles.”

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U.S. Hybrid Work Model

Effective September 5, 2023, employees in office-based positions in the U.S. will be working a Hybrid work consisting of three total days on-site per week, Monday – Thursday, although the specific days may vary by site or organization, with Friday designated as a remote-working day, unless business critical tasks require an on-site presence.This Hybrid work model does not apply to, and daily in-person attendance is required for, field-based positions; facility-based, manufacturing-based, or research-based positions where the work to be performed is located at a Company site; positions covered by a collective-bargaining agreement (unless the agreement provides for hybrid work); or any other position for which the Company has determined the job requirements cannot be reasonably met working remotely. Please note, this Hybrid work model guidance also does not apply to roles that have been designated as “remote”.

The salary range for this role is

$153,800.00 – $242,200.00

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.

The successful candidate will be eligible for annual bonus and long-term incentive, if applicable.

We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at https://jobs.merck.com/us/en/compensation-and-benefits.

You can apply for this role through https://jobs.merck.com/us/en (or via the Workday Jobs Hub if you are a current employee). The application deadline for this position is stated on this posting.

San Francisco Residents Only: We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance

Los Angeles Residents Only: We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance

Search Firm Representatives Please Read Carefully 
Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails. 

Employee Status:

Regular

Relocation:

Domestic

VISA Sponsorship:

Yes

Travel Requirements:

10%

Flexible Work Arrangements:

Not Applicable

Shift:

Not Indicated

Valid Driving License:

No

Hazardous Material(s):

n/a

Required Skills:

Clinical Development, Clinical Testing, Data Analysis, Data Engineering, Data Modeling, Data Visualization, Drug Development, Mathematics Modeling, Modeling Simulations, Modeling Software, Monoclonal Antibodies, Pharmacodynamics, Pharmacokinetics, Pharmacometrics, PKPD Modeling, Quantitative Models, Regulatory Submissions, Systems Biology

Preferred Skills:

Job Posting End Date:

07/18/2025

*A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.