This role pays less than
95%
of similar roles. Most pay
$126,800–$194,727
— the shaded band above.
At the midpoint, this role pays about
$102k
versus about
$161k
for comparable roles.
Based on 240 similar postings.
Employer
About Thermo Fisher
Thermo Fisher Scientific is the world leader in serving science, providing analytical instruments, equipment, reagents, consumables, software, and services for life sciences research, pharmaceutical manufacturing, and diagnostics. Industry: Life Sciences & Laboratory Equipment
Thermo Fisher currently has
9 open roles
on FindRole.
Listed pay typically runs
$93,800–$127,800
across 7 roles with salary data.
Scientist III, Data Scientist, Bioprocess Digital Twin will join the team to develop and deploy sophisticated data science solutions that drive operational efficiencies and enhance decision-making. The role focuses on creating a practical bioprocess digital twin roadmap, including data strategy, model development, validation, and cross-functional adoption. Key responsibilities include building predictive simulation capabilities for CHO cell culture and upstream bioprocess workflows like fed-batch and perfusion applications. You will develop hybrid modeling workflows combining mechanistic approaches—such as stoichiometric, kinetic, MFA/FBA, and mass balance methods—with AI, machine learning, and Bayesian optimization. The role requires advanced Python skills for scientific computing and experience with complex datasets including time-series process data and omics data. This position addresses the technical challenge of simulating nutrient depletion, cell growth, and scale-up behavior to prioritize experimental paths in biomanufacturing environments.
What does a Data Scientist earn in New York?
Median $173200 from 38 postings across 17 companies.
Lead the development of digital twin models for CHO cell culture and upstream bioprocess workflows.
Build predictive simulation capabilities for nutrient depletion, metabolite accumulation, cell growth, and process drift.
Develop hybrid modeling workflows combining mechanistic approaches like kinetic modeling with AI and machine learning.
Enable virtual experimentation by simulating feed strategies and process conditions to prioritize experimental paths.
Define data requirements, validation strategies, model governance practices, and success criteria for simulation workflows.
Support real-time forecasting by using bioreactor data to detect deviations and recommend corrective actions.
Translate complex scientific challenges into deployable digital solutions across the enterprise.
What we're looking for
Master's degree in Biochemical Engineering, Chemical Engineering, Biotechnology, Computational Biology, Systems Biology, Bioinformatics, Cell Biology, or a related field.
A PhD in a relevant discipline is preferred.
Strong industry or advanced academic experience in bioprocess development, cell culture, process modeling, digital biomanufacturing, or computational biology.
Deep understanding of upstream bioprocessing, including CHO cell culture, metabolism, feeding strategies, and bioreactor operations.
Advanced Python experience for scientific computing, simulation, data analysis, workflow automation, and visualization.
Experience developing mechanistic, statistical, machine learning, optimization, forecasting, or hybrid modeling workflows for biological systems.
Experience working with complex bioprocess datasets including time-series process data, offline assays, and omics data.
Demonstrated ability to lead cross-functional technical projects and translate scientific challenges into deployable digital solutions.
Python
R
Machine Learning
Genomics
Data Science
Cloud Computing
Large Language Models
Generative AI
Statistics
Computational Biology
Toxicology
Pharmacology
Molecular Profiling
Analytical Workflows
Dashboards