Associate Principal Scientist, Downstream Bioprocess Modeling, Digital Insights

Job Description

We are a global biopharmaceutical leader with a different portfolio of prescription medicines, oncology, vaccines and animal health products. We are driven by our purpose to develop and deliver innovative products that save and improve lives. With 69,000 employees operating in more than 140 countries, we offer state-of-the-art laboratories, plants and offices that are designed to inspire our employees as we learn, develop and grow in our careers.  We are proud of our over 125 years of service to humanity and continue to be one of the world’s biggest investors in Research & Development.

We are seeking an Associate Principal Scientist to join our Process Modeling & Analytics team within the Development Sciences and Clinical Supply Digital Technologies - Digital Insights organization (DDT-DI). Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio.

The DSCS Digital Technologies organization is responsible for the invention and application of new digital tools/workflows to support scientists across drug substance development, drug product development and analytical development. We aspire to embed digital technologies into the fabric of DSCS culture to drive transformational impact across the CMC space. In this Associate Principal Scientist role, the successful candidate will apply mechanistic modeling and numerical simulation to downstream biologics processes, with a focus on preparative chromatography and adjacent unit operations. They will build calibrated column and unit-operation models to guide resin and mode selection, gradient and loading strategy, cycle design, filter sizing, robustness assessments, and scale-up decisions across a multi-modality pipeline.

The successful candidate will play a technical leadership role in embedding mechanistic downstream modeling into DSCS decision-making—partnering closely with DSP scientists, process engineers, and DS technical leads to translate model outputs into actionable purification and manufacturing decisions. As a senior member of the Process Modeling & Analytics team, they will also mentor junior scientists, help shape the group’s downstream modeling roadmap, and grow the practice of mechanistic simulation across the pipeline.

Responsibilities:

• Build, calibrate, and validate mechanistic chromatography models in CADET, GoSilico, or equivalent tools for capture, polishing, and viral clearance steps.
• Design isotherm and mass-transfer parameter estimation studies with DSP experimentalists: plan the calibration dataset (breakthrough, gradient elution, tracer runs) needed to identify model parameters.

• Apply calibrated models to guide resin and mode selection, gradient and loading strategy, cycle design, pool criteria, robustness assessments, and scale-up decisions.
• Extend mechanistic modeling to adjacent downstream unit operations such as viral inactivation kinetics, UF/DF, and depth filtration.
• Own end-to-end modeling project execution: problem framing, experimental design for calibration, solver setup, validation against experimental data, and clear communication of predictions and their limitations to cross-functional stakeholders.
• Mentor junior scientists on the Process Modeling & Analytics team; grow their technical judgment in mechanistic modeling, numerical methods, and simulation-based decision making.
• Shape the team's downstream modeling roadmap and establish practical standards for model development, calibration, validation, and reuse across the portfolio.

Education Minimum Requirement:

• Ph.D. in Chemical Engineering, Bioengineering, or a closely-related engineering/physical sciences field with at least 3 years of industrial/pharmaceutical or relevant experience.
• M.S. in Chemical Engineering, Bioengineering, or a closely-related engineering/physical sciences field with at least 5 years of industrial/pharmaceutical or relevant experience.
• B.S. in Chemical Engineering, Bioengineering, or a closely-related engineering/physical sciences field with at least 7 years of industrial/pharmaceutical or relevant experience.

Required Experience and Skills:

• Chemical engineering training (or closely related) with deep grounding in preparative chromatography, transport in porous media, and separation science.
• Hands-on experience building, calibrating, and validating mechanistic chromatography models in CADET, GoSilico, or equivalent numerical simulation platforms.
• Working knowledge of rate models (general-rate, lumped, transport-dispersive) and isotherm formulations (Langmuir, SMA, colloidal/multicomponent variants), and their assumptions and limits.
• Practical experience with parameter estimation: designing calibration experiments, fitting isotherms and mass-transfer coefficients, and quantifying identifiability and uncertainty.
• Applied understanding of preparative chromatography for biologics (ion exchange, HIC, mixed-mode, affinity), viral clearance, and how column operating parameters translate to product quality and yield.
• Track record of applying mechanistic models to real process decisions in resin/mode selection, gradient design, robustness, or scale-up.
• Ability to validate simulations against experimental data and to articulate model credibility, sensitivities, and uncertainty to advise action and decision.
• Scientific leadership and mentorship experience; comfortable growing modeling capability in others rather than only doing the work personally.

Preferred Experience and Skills:

• Direct experience in an industrial biologics setting (mAbs, viral vectors, vaccines, or other modalities) as a process development scientist or process engineer.
• Experience with mechanistic modeling of adjacent downstream unit operations (viral inactivation kinetics, UF/DF, or depth filtration).
• Python fluency for pre-/post-processing, workflow automation, and coupling of mechanistic tools with data pipelines and DOE workflows.
• Familiarity with machine learning for downstream modeling: surrogates for expensive mechanistic runs, hybrid mechanistic/ML models, or ML-assisted parameter estimation.
• Experience with high-throughput chromatography (HT-PD) or PAT data streams and their integration into model calibration and validation workflows.
• Familiarity with continuous / connected downstream processing (multi-column setups, cycle scheduling, dynamic control).
• Prior use of mechanistic downstream modeling in technology transfer, process characterization, or troubleshooting at scale.
• Prior contributions to technology transfer, process robustness assessments, or troubleshooting using modeling and simulation are a strong plus.

Required Skills:

Preferred Skills:

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The salary range for this role is

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.

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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

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