Predictive Modeler - Drug Product Development

Key Skills

Aartificial intelligence, Data Analysis, Design, Effective communication skills, Leadership Skill, Machine learning techniques, Python Programming, R Programming, SAS, SQL

Job Description

Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams rich in diversity. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.

Predictive Modeler – Drug Product Development

Location: New Jersey, United States

Description

The Predictive Design team is part of BMS’ Integrated Technology and Engineering group within Drug Product Development where we apply and develop state-of-the-art capabilities in predictive science, computational modeling, and AI/ML solutions to improve patient’s lives. We partner globally with internal stakeholders to drive analytic and modeling capabilities, accelerating strategic and technical insights to enable decision making and advance our portfolio. We accomplish this through a focus on the use of data by collaborating seamlessly with internal teams across the globe and with the external scientific and regulatory communities. The predictive modeler is a very important role to advance modeling strategies and innovative methods generating valuable insights using internal and external data sources. The predictive modeler will provide technical leadership through mechanistic modeling, model based DOEs, and material science. With the modeling first mindset, the successful candidate will design and develop predictive models for device delivery systems, powder-, fluid-, & polymer-based systems and multiphase flows in drug product development and manufacturing. This work will advance the field of predictive design in the pharmaceutical industry as well as enhance the understanding of product development. The selected candidate will work in a fast-paced multidisciplinary environment as the competitive landscape and modeling strategies keep changing rapidly.

Role Responsibilities

• Contribute to on-going portfolio projects within multiple therapeutic areas
• Design, develop, and perform advanced computational techniques to understand the impact of device design, process variables, and material properties on products attributes
• Contribute to the end-to-end modeling strategy for virtual design of unit operations
• Work with ML scientists and material scientists to guide the development of our predictive models
• Demonstrate independent thinking by proposing and implementing new experimental designs and concepts (e.g. Data Rich Experimentation, model based DOEs)
• Plan and execute experimental studies in collaboration with experimental experts to support/validate modeling efforts
• Create prototypes, predictive models, and demonstrations to help articulate the value of insights for advancing science and the business
• Keep abreast of relevant literature and maintain a high level of expertise in own field such as digitalization and Pharma 4.0 mindset
• Proactively promote an open attitude towards new technologies and a culture towards a Lab-of-the-Future mindset
• Ensure integration of new technologies (e.g. Digitalization, Automation, PAT) and department initiatives into daily work to enhance productivity
• Conceptualize approaches, identify capability gaps, and set goals within drug product design
• Communicate and present results to an interdisciplinary team of experimental, modeling scientist and machine learning colleagues at company meetings and strategic program readouts
• Work with external partners (e.g., universities, research organizations) and vendors (e.g., software companies) to execute and deliver well-defined modeling projects
• Ensure compliance with all regulations and policies and perform all work in a safe manner
• Maintain proper records (electronic notebooks, batch sheets, etc.) per relevant SOPs
• Prepare and present research in leading journals and conferences
• Maintain a well-documented, reusable codebase, and traceable model history

Qualifications

• Education Minimum Requirement: PhD with 0-5 years of experience. The PhD could be in computational science & engineering, applied mathematics, chemical engineering, mechanical engineering and/or materials science & engineering or a related quantitative discipline.

• Required skills:

Research experience in the field of AI/ML, QSAR, engineering and computational modeling techniques is required (e.g., Bayesian models, parallel computing, finite element modeling FEM, discrete element method DEM, computational fluid dynamics CFD, flowsheet modeling, statistical techniques, population-balance models PBM, and first-principle engineering models)

Ability to build and manage models to predict future outcomes using data that is often incomplete and/or imperfect and contained in disparate source

Proficiency in Python (or other scientific programming such as SAS, R or SQL) to curate and analyze historical and data at scale and to implement new metrics

Data analysis experience with heterogeneous data types and familiarity of big data, analytics and/or data mining environments; Ability to identify trends, identify outliers and find patterns

In collaboration with scientific and business partners, able to develop hypotheses and test them with careful experiments

Experience with data visualization (e.gs., R, DASH, Spotfire, Tableau) and statistical tools and models (e.gs. PCA, ANOVA, Minitab, JMP)

Ability to perform in-depth data analysis and present results and conclusions to product development and leadership teams

Excellent verbal and written communication skills

Detail oriented with strong documentation skills

• Preferred/Desired skills:

Examples of your work, such as GitHub repository or publications that can showcase your skill

Experience working in a cloud environment and using cloud-based services is desirable

Experience in cheminformatic, sequence-based analysis methods, and natural language processing/text mining and/or exposure would be a plus

Experience with Jupyter Notebooks and/or Shiny applications is a plus

Working knowledge of key pharmaceutical unit operations both in drug substance and drug product including, but not limited to, crystallization, mixing, milling, granulation (wet and dry), encapsulation, tablet compression, and coating

Familiarity with a variety of material characterization and Process Analytical Technology (PAT) tools would be a plus