Associate Director, Quantitative Systems Pharmacology in Cambridge

The Associate Director, Quantitative Systems Pharmacology (QSP) will support a rapidly expanding clinical portfolio of promising RNAi therapeutics. The candidate in this position should be well versed in systems pharmacology, clinical pharmacology, PK and PD with the ability to critically evaluate and interpret data and conduct data analysis. In this role, the candidate is expected to show a high degree of scientific curiosity and critical thinking ability with good oral and written communication skills, and the ability to work independently with minimal supervision in a highly matrixed environment. The Associate Director, QSP will champion the application of QSP modeling and simulation approaches to answer key drug development questions across the portfolio spanning all stages of drug development.

Key Responsibilities

• With minimal supervision, strategize, formulate and execute QSP modeling and simulation approaches for insights into dose selection, trial design, mechanistic understanding of the interaction between disease, biomarker and treatment response, predicting clinical outcomes etc.
• Closely partner with clinical pharmacologists, pharmacometricians, research, clinical, non-clinical modelers and other relevant R&D functions.
• Work closely with research and clinical functions to identify and interpret preclinical and clinical data to guide in data generation relevant for building and refining QSP models.
• Build and utilize QSP models of biological or pathophysiological processes to understand the disease pathways and effect of treatment intervention.
• Independently analyse, interpret, and report QSP modeling results for internal decision making.
• Stay informed about advancements in QSP modeling methodologies.
• Be a technical leader within the group and mentor team members.
• Establish and advance visibility and impact of QSP modeling internally and externally through publications in peer-reviewed journals and presentations at scientific meetings.
• Explore synergy between QSP and other modeling approaches to inform drug development decisions.

Qualifications

• Candidates with a Ph.D (or equivalent) degree in applied mathematics, biomedical engineering, systems biology/pharmacology or related discipline.
• At least 5 years of experience in the pharmaceutical industry, consulting, or regulatory agency.
• Track record of application of QSP modeling to impact drug development decisions.
• Good understanding of systems biology, physiology, pharmacokinetics and pharmacodynamic principles.
• Strong foundation of theory, principles and statistical aspects of modeling and simulation including differential equation based modeling and parameter estimation/optimization approaches.
• Extensive hands-on experience with common QSP modeling platforms such as R, MATLAB, Simbiology etc.
• Experience in translating and summarising QSP modeling outcomes into information understood by project teams.
• Open-minded, receptive to input and a strong team player.
• Self-driven, able to multi-task and manage time-sensitive deliverables.
• Strong interpersonal skills with the ability to collaborate and work in a matrix environment.
• Strong written and oral presentation skills.

About Alnylam

We are the leader in RNAi therapeutics – a revolutionary approach with the potential to transform the lives of people with rare and common diseases. Built on Nobel Prize-winning science, Alnylam has delivered the breakthroughs that made RNAi therapeutics possible and are just at the beginning of what’s possible. Our deep pipeline, late-stage programs, and bold vision reflect our core values: fierce innovation, passion for excellence, purposeful urgency, open culture and commitment to people. We're proud to be a globally recognised top employer, where an authentic, inclusive culture and breakthrough thinking fuel one another. At Alnylam, we commit to an inclusive recruitment process and equal employment opportunity. Qualified applicants will receive consideration for employment without regard to their sex, gender or gender identity, sexual orientation, race, colour, ethnicity, national origin, ancestry, citizenship, religion, creed, physical or mental disability, pregnancy status or related conditions, genetic information, veteran or military status, marital or familial status, political affiliation, age, or any other factor protected by federal, state, or local law. Alnylam is an E-Verify Employer.