FaireSan Francisco, CA$211k-$291k2h ago
Johnson & JohnsonPosted 3w ago
Sr. Scientist, Spectroscopy Lifecycle Support at Johnson & Johnson scores 83 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
R&D Product DevelopmentJob Sub Function:
R&D DigitalJob Category:
Scientific/TechnologyAll Job Posting Locations:
Latina, ItalyJob Description:
We are seeking a Sr. Scientist in Spectroscopy and PAT Model Lifecycle Support to join ANVIL, the ApplicatioNs, Validation, Integration, and Lifecycle group within the Process Science Modelling and Data (PSMD) organization in Manufacturing Science and Technology (MSAT). ANVIL supports the development, validation, deployment, integration, troubleshooting, and lifecycle maintenance of models used to support manufacturing, including models deployed or intended for use in GMP environments.
This role will focus on spectroscopic Process Analytical Technology models, with particular emphasis on near-infrared spectroscopy and Raman spectroscopy. The successful candidate will help sustain and develop chemometric models that support product and process understanding, real-time or near-real-time decision making, and reliable operation of model-enabled workflows across manufacturing and laboratory environments.
The ideal candidate will bring hands-on experience with spectroscopy, multivariate modeling, and data analysis, along with a practical interest in applying these skills in regulated pharmaceutical manufacturing. The candidate should be comfortable working across scientific, technical, quality, and operational boundaries, translating complex model behavior into clear decisions, documentation, and recommendations.
Key Responsibilities
Validation, Deployment, and GMP Lifecycle Support
- Support validation and implementation of chemometric models used in GMP or GMP-adjacent environments.
- Use sound statistical reasoning to assess model performance, residuals, prediction error, bias, variance, outliers, and sources of model drift.
- Contribute to model validation protocols, validation reports, technical transfer packages, model maintenance plans, and lifecycle documentation.
- Troubleshoot deployed models, including investigation of spectral quality issues, unexpected predictions, drift, transfer challenges, and model or workflow failures.
- Support deployment of models into operational workflows, including collaboration with platform, laboratory, manufacturing, quality, IT, and automation partners.
- Help ensure model lifecycle activities are documented, traceable, and aligned with intended use, regulatory expectations, and internal quality procedures.
- Contribute to best practices for spectroscopic model development, validation, maintenance, troubleshooting, and lifecycle management.
Chemometric Model Development and Evaluation
- Develop, evaluate, and document chemometric models for spectroscopic PAT applications, including NIR, Raman, or related spectroscopic modalities.
- Apply multivariate data analysis methods such as Principal Component Analysis, Partial Least Squares regression, classification approaches.
- Utilize statistics to develop thresholds on key model outputs.
Other Tasks
- Learn and apply Python-based workflows for data analysis, model development, model evaluation, visualization, and automation.
- Use git or similar version control practices to support traceability, collaboration, and maintainable scientific code.
- Develop reusable analysis patterns, scripts, notebooks, and templates that improve consistency and efficiency across model development and lifecycle activities.
Required Qualifications
- Degree or higher in Chemistry, Chemical Engineering, or a related scientific or technical discipline.
- BS with 4+ years experience
- MS with 2+ years experience
- PhD, experience preferred
- Deep expertise with spectroscopy in at least one field or modality, such as NIR, Raman, MIR, UV-Vis, fluorescence, or related field.
- Experience developing, evaluating, or applying models to scientific, analytical, process, manufacturing, or laboratory data.
- Statistical fluency, including understanding of variation, bias, error, residuals, confidence, model performance, and experimental reasoning.
- Experience with at least one programming language, such as Python, MATLAB, R, or a similar scientific computing environment.
- Willingness to learn and apply Python for chemometric modeling, data analysis, automation, and reproducible model evaluation.
- Strong communication skills and the ability to work effectively in cross-functional scientific, technical, quality, and manufacturing teams.
- Ability to document technical work clearly and support decisions in a regulated or quality-sensitive environment.
Preferred Qualifications
- Direct experience with NIR or Raman spectroscopy in pharmaceutical, biopharmaceutical, chemical, food, materials, or other process-oriented applications.
- Experience developing or maintaining chemometric models for PAT, real-time release, process monitoring, laboratory testing, or manufacturing decision support.
- Practical experience with PLS regression, PCA-based monitoring, spectral preprocessing, calibration transfer, model robustness assessment, spectral quality attributes, or instrument-to-instrument transfer.
- Experience supporting models in GMP, GxP, regulated, quality-controlled, or highly documented environments.
- Experience with Python libraries used for scientific computing, data analysis, or modeling, such as pandas, NumPy, scikit-learn, SciPy, matplotlib, plotly, or related tools.
- Familiarity with git-based collaboration, pull requests, code review, version control, or reproducible analytical workflows.
- Familiarity with pharmaceutical manufacturing, process validation, analytical method validation, quality systems, or regulatory expectations for model-supported testing.
Required Skills:
Preferred Skills:
Analytical Reasoning, Critical Thinking, Data Savvy, Digital Fluency, Digital Strategy, Engineering, Product Design, Product Development, Product Improvements, Product Portfolio Management, Report Writing, Research and Development, Safety-Oriented, SAP Product Lifecycle Management, Statistics, Technical Credibility, Technologically Savvy
The anticipated base pay range for this position is:
€48,600.00 - €77,395.00
Benefits:
In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees’ and companies’ performance of the previous calendar year, or sales commissions. Moreover, we offer vacation days, parental leave for a minimum of 12 weeks, bereavement leave, caregiver leave, volunteer leave, well-being reimbursement, programs for financial, physical and mental health. We also offer service anniversary and recognition awards, and subject to the terms of their respective plans, employees - and in some location’s eligible dependents - can participate in several insurance plans. For more information, visit Employee benefits | Supporting well-being & career growth | Johnson & Johnson Careers.
*This is for informative purposes only. Amounts and actual benefits may vary by location and are subject to change.
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Skills and AI tools this role asks for
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- How do you decide that one model's output is better than another's for a given task?
- Walk me through how you've used scikit-learn in your day-to-day work.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
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