This role pays less than
99%
of similar roles. Most pay
$126,800–$189,436
— the shaded band above.
At the midpoint, this role pays about
$84k
versus about
$158k
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 II, Data Scientist, Bioprocess Digital Twin will join the data science team to develop advanced analytics and AI solutions for complex business challenges. The role involves building datasets, models, and workflows to support practical digital twin applications in upstream bioprocessing. Key responsibilities include developing predictive modeling and simulation workflows for CHO cell culture, specifically focusing on nutrient consumption, metabolite accumulation, cell growth, viability, and productivity. You will analyze time-series bioreactor data, offline assay results, and process metadata while collaborating with engineers and software teams to document assumptions and plan experiments. The position requires proficiency in Python for scientific computing, data visualization, and simulation. Candidates must possess strong quantitative skills and knowledge of mechanistic, statistical, or machine learning modeling approaches to solve problems related to bioprocess behavior and biological systems.
What does a Data Scientist earn in New York?
Median $173200 from 38 postings across 17 companies.
Develop predictive modeling and simulation workflows for CHO cell culture and upstream bioprocess digital twin applications.
Build, test, and evaluate models for key behaviors like nutrient consumption, metabolite accumulation, and cell growth.
Analyze complex bioprocess datasets including time-series bioreactor data, offline assay results, and process metadata.
Use Python and scientific computing tools to process data, run simulations, and visualize results.
Translate scientific questions into clear analyses and useful modeling results for business applications.
Document modeling assumptions and summarize results to support experimental planning with cross-functional teams.
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.
Bachelor's degree plus 2 years of industry experience in relevant scientific domains.
Relevant academic or industry experience in upstream bioprocessing, cell culture, process modeling, computational biology, or data science.
Strong understanding of CHO cell culture and upstream bioprocessing concepts including bioreactor operation and cell growth metrics.
Hands-on Python experience for scientific computing, data analysis, visualization, simulation, or model implementation.
Experience working with bioprocess or biological datasets, specifically time-series data and assay results.
Familiarity with mechanistic, statistical, machine learning, or hybrid modeling approaches for biological systems.
Strong analytical and problem-solving skills with attention to data quality and scientific detail.
Python
R
SQL
Generative AI
Agentic AI
Prompt Engineering
RAG
Machine Learning
Pandas
NumPy
Scikit-learn
TensorFlow
PyTorch
Tableau
Power BI
Data Warehousing
Statistics
Feature Engineering
Python
R
Machine Learning
Genomics
Data Science
Cloud Computing
Large Language Models
Generative AI
Statistics
Computational Biology
Toxicology
Pharmacology
Molecular Profiling
Analytical Workflows
Dashboards
Process Engineering
Chemical Engineering
Python
MATLAB
C++
COMSOL
Scale-Up Suite
M-Star
cGMP
Process Analytical Technology (PAT)
Flow Chemistry
Unit Operations
Distillation
Crystallization
Process Safety
Technical Transfer
Automation