below marketmost similar roles pay hereabove market
This role pays less than
58%
of similar roles. Most pay
$162,000–$238,789
— the blue band above.
At the midpoint, this role pays about
$180k
versus about
$200k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
130 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 122 roles with salary data.
JOB TITLE: Senior Scientist II / Principal Research Scientist I - Analytical R&D
The Senior Scientist II / Principal Research Scientist I - Analytical R&D joins the Analytical Research & Development team to provide scientific and technical leadership for analytical data science, artificial intelligence, and machine learning initiatives. This role involves partnering with analytical scientists to frame scientific questions, engineer complex analytical data, and develop fit-for-purpose models, visualizations, and automated workflows. The successful candidate will utilize Python, R, SQL, and libraries for statistical modeling and data visualization to accelerate pharmaceutical development. Key responsibilities include mentoring scientists, establishing data governance standards, and managing a portfolio of data products. The work focuses on solving CMC analytical challenges for small molecule medicines, spanning from discovery candidate selection through early-stage clinical development by converting innovative concepts into sustainable, reproducible digital solutions.
Provide scientific and technical leadership for analytical data science, AI/ML, and data automation initiatives.
Consult with analytical scientists to define scientific questions, assess data readiness, and select analytical approaches.
Lead the end-to-end delivery of data products, including model development, validation, and deployment.
Apply statistical, machine-learning, and generative-AI methods to complex analytical data while ensuring scientific interpretability.
Engineer and curate structured and unstructured data from laboratory instruments to improve data quality and traceability.
Design and deliver intuitive applications, dashboards, and automated workflows to reduce manual effort for scientists.
Mentor and develop data scientists and scientific colleagues through technical coaching and peer reviews.
Establish standards for model validation, software development, data governance, and version control.
What we're looking for
Bachelor’s, Master’s, or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Chemometrics, Computational Science, Analytical Chemistry, Pharmaceutical Sciences, or a related discipline.
Minimum of 14 years experience for BS holders, 12 years for MS holders, or 6 years for Ph.D. holders in a relevant industry.
Demonstrated experience applying data science, AI/ML, statistics, or advanced analytics to scientific, pharmaceutical, biotechnology, or chemical R&D problems.
Advanced proficiency in Python and/or R with strong knowledge of data-science libraries, statistical modeling, and reproducible analytical workflows.
Working knowledge of SQL, relational data concepts, APIs, and modern data-management practices for integrating and analyzing complex data.
Demonstrated ability to build production-quality analytical applications, dashboards, and automated workflows using tools like Streamlit, Shiny, or cloud platforms (preferred).
Demonstrated leadership through mentoring, technical direction, project portfolio leadership, and cross-functional influence.
Knowledge of pharmaceutical development, CMC, analytical laboratory workflows, or regulated environments (preferred).
Experience with computational chemistry and molecular modeling approaches (preferred).
Python
R
Machine Learning
Generative AI
TensorFlow
PyTorch
scikit-learn
Bayesian Methods
ML Ops
Agile
Stan
PyMC
brms
Data Pipelines
Statistical Modeling
Causal Inference
Cloud-native ML
Data Governance
Simulation Studies
C/C++
Python
Julia
Rust
CUDA
GPU Programming
Digital Signal Processing
Image Processing
Embedded Software
Firmware Engineering
Scientific Computing
Estimation Theory
Optimization Theory
Linear Algebra
Numerical Analysis
Probability Theory
Stochastic Models
Graphical Models
Rapid Prototyping
Deep Learning
Python
C++
LLMs
Diffusion Models
Unsupervised Learning
Genomics
Proteomics
Drug Discovery
Bioinformatics
Molecular Dynamics
Chemical Engineering
Structural Biology
Neural Networks
Foundation Models
Multimodal Learning
Information Retrieval
Machine Learning
Physical AI
Dense Retrieval
CLIP
Embeddings
Active Learning
Data Curation
Lidar
Radar
Production-grade Software
Model Training Pipelines
Neural Search
Multimodal Indexing
Machine Learning
Deep Learning
Natural Language Processing
LLMs
Embedding Models
Rerankers
Distributed Training
Information Retrieval
Neural Networks
RAG
Semantic Search