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Job Description

OBJECTIVE:

Synthetic Molecule Process Development (SMPD) is responsible for developing robust and cost-effective processes for manufacturing new small molecule pharmaceuticals, ensuring high standards of purity and quality through advanced process design and control strategies. The Staff Engineer – Process Modeling will join a dynamic and innovative team of engineers and scientists within SMPD’s Process Engineering & Technology Group, focusing on crystallization modeling and particle engineering.

The successful candidate will have a strong background in crystallization science, particle engineering, and process modeling, tackling challenges related to crystal nucleation, growth, polymorphism, and particle size distribution control. They will leverage expertise in thermodynamics, kinetics, transport phenomena, and population balance modeling to develop in-silico approaches for crystallization process design and optimization. Additionally, the Staff Engineer will be involved in scale-up and scale-down modeling of crystallization, wet milling, and solid-liquid separation processes, integrating both first-principles and data-driven models.

The role includes the application of process analytical technologies (PAT) in combination with mathematical models (both mechanistic and statistical) to enhance process understanding and enable data-rich experimentation. These approaches will drive the design, optimization, scale-up, and troubleshooting of crystallization and particle engineering processes, ensuring robust pharmaceutical manufacturing through digital and in-silico methodologies.

                                   

ACCOUNTABILITIES:

Contributes to the design, development, optimization, and scale-up of crystallization and particle engineering processes for synthetic molecule drug substances using process modeling and simulation principles.

Utilizes advanced process modeling tools, population balance models (PBM), and digital twin functionalities, implementing model-based design of experiments (MBDoE) for process characterization, risk assessment, and control strategy development.

Develops experimental designs and workflows for model development, validation, and verification, with a focus on crystal nucleation, growth kinetics, polymorphism control, and particle size distribution (PSD) engineering.

Collaborates with cross-functional teams and external partners to develop and deploy digital twins of crystallization, wet milling, and solid-liquid separation unit operations.

Partners with Automation, Manufacturing, Process Engineers, and PAT experts to develop modeling and simulation (M&S) solutions that can be deployed across the global organization for in-silico process design, development, and optimization.

Recommends, justifies, and implements in-silico tools and an "in-silico first” approach for crystallization and particle engineering.

Authors reports, and peer-reviewed manuscripts, conference presentations.

EDUCATION, EXPERIENCE AND SKILLS:

Education and Experience:

Required:

Bachelor’s degree in Chemical Engineering, Pharmaceutical Sciences, or a related field with 5+ years of relevant industry experience.

Master’s degree in Chemical Engineering, Pharmaceutical Sciences, or a related field with 3+ years of relevant industry experience.

Ph.D. in Chemical Engineering, Pharmaceutical Sciences, or a related field with 0+ years of relevant industry experience.

Strong knowledge and understanding of crystallization process modeling, particle engineering, and polymorph control.

Strong understanding of population balance models (PBM) for crystal nucleation, growth, breakage, and aggregation dynamics.

Expertise in transport phenomena and thermodynamics as applied to crystal growth, supersaturation control, and solid-liquid equilibria.

Experience with commercially available crystallization and particle engineering modeling software, such as gPROMS Formulated Products, Dynochem, or Ansys Fluent.

Experience with computational fluid dynamics (CFD) modeling for mixing and solid suspension applications, using software like Ansys Fluent, Star-CCM+, or MStar CFD.

Proficiency in data integration from sensors, controllers, and industrial systems, ensuring real-time process monitoring and model-based control.

Experience with programming tools such as MATLAB, Python, R, SQL, and adherence to good coding practices for model implementation and automation.

Preferred:

Experience with multivariate analysis (MVA) and Principal Component Analysis (PCA) for crystallization process optimization.

Knowledge of process analytical technologies (PAT) such as FBRM, Raman, PVM, and UV-Vis for real-time monitoring of crystallization processes.

Hands-on experience in wet lab crystallization and particle size distribution (PSD) characterization.

Familiarity with Good Manufacturing Practices (cGMP) and regulatory requirements for model-based submissions.

Understanding of digital twin applications in process development and scale-up.

Experience in machine learning (ML) or AI-assisted modeling approaches for process optimization.

Knowledge and Skills:

Analytical and Problem-Solving Skills – Ability to troubleshoot crystallization and particle engineering challenges, analyze model-based results, and propose effective solutions.

Teamwork – Capable of working effectively in a highly cross-functional environment, engaging with process engineers, chemists, data scientists, and regulatory teams.

Communication Skills – Clearly conveys technical concepts, summarizes modeling insights concisely, and adjusts communication style based on audience. Strong technical writing skills for scientific reports, regulatory documents, and publications.

Organization and Time Management – Ability to prioritize multiple tasks and successfully manage work across individual, departmental, and corporate goals.

Knowledge Sharing – Effectively captures and transfers organizational knowledge, contributing to model libraries, process databases, and training programs for broader adoption of in-silico tools.

Resource Management – Manages time and technical resources efficiently, including external collaborations with vendors, research institutions, and software providers.

External Involvement – Actively contributes to the scientific community through conference presentations, scientific publications, and professional workshops.

Technical Expertise – Demonstrates subject matter expertise in crystallization modeling, population balance modeling, and in-silico process development, leveraging advanced simulation tools for process design and optimization.

Takeda Compensation and Benefits Summary

We understand compensation is an important factor as you consider the next step in your career. We are committed to equitable pay for all employees, and we strive to be more transparent with our pay practices. 

For Location:

Boston, MA

U.S. Base Salary Range:

$108,500.00 - $170,500.00

The estimated salary range reflects an anticipated range for this position. The actual base salary offered may depend on a variety of factors, including the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job. The actual base salary offered will be in accordance with state or local minimum wage requirements for the job location. 

U.S. based employees may be eligible for short-term and/ or long-term incentives. U.S. based employees may be eligible to participate in medical, dental, vision insurance, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, a tuition reimbursement program, paid volunteer time off, company holidays, and well-being benefits, among others. U.S. based employees are also eligible to receive, per calendar year, up to 80 hours of sick time, and new hires are eligible to accrue up to 120 hours of paid vacation. 

EEO Statement

Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.

LocationsBoston, MA

Worker TypeEmployee

Worker Sub-TypeRegular

Time TypeFull time

Job Exempt

Yes

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.