below marketmost similar roles pay hereabove market
This role pays less than
77%
of similar roles. Most pay
$145,200–$214,500
— the blue band above.
At the midpoint, this role pays about
$142k
versus about
$180k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$113,000–$201,250
across 38 roles with salary data.
JOB TITLE: Senior Data Scientist, Biologics Discovery
The Senior Data Scientist, Biologics Discovery joins the Data Science team to support the Biologics Discovery organization. This role focuses on building data-facing machine learning capabilities, including featurization and the creation of model-ready datasets, to enhance molecular design and property-prediction models. You will analyze heterogeneous biologics discovery data, such as antibody sequences and biophysical assay results, to identify data quality issues and risks like distribution shift. Key responsibilities include curating standardized, traceable datasets and collaborating with discovery scientists to translate scientific questions into analytical requirements. Required skills include a Master's or Ph.D. in a relevant field, proficiency in Python, SQL, PyTorch, and scikit-learn, and experience with complex biomedical data. The work addresses the technical challenge of making fast-growing biologics data seamlessly available for training predictive models.
What does a Data Scientist earn?
Median $173200 from 338 postings across 66 companies.
Apply modern data science tools to explore, integrate, and characterize heterogeneous biologics discovery data.
Translate scientific questions and decision points into clear data and analytical requirements for discovery scientists.
Identify data quality issues, biases, and risks such as leakage or distribution shift in downstream modeling.
Develop featurization approaches and curate standardized, traceable, and model-ready datasets from biologics assay and sequence data.
Specify features, labels, and levels of aggregation with data engineers to ensure models have necessary data representations.
Curate, document, and version datasets to ensure modeling is reproducible and traceable.
Partner with In Silico Discovery to hand off standardized training datasets and align on modeling ownership.
Collaborate with ontology and MLOps colleagues to ensure datasets carry consistent semantics and move reliably into use.
What we're looking for
Master's or Ph.D. in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, or a related field.
At least 2 years of applied ML experience, including model development, evaluation, and dataset curation on complex scientific or biomedical data.
Strong proficiency with Python, SQL, and the modern ML stack such as PyTorch and scikit-learn.
Experience turning complex, heterogeneous experimental data into robust features and training sets with exposure to cloud training and data infrastructure.
Sound understanding of evaluation, validation, and the risks of leakage and distribution shift.
Ability to collaborate effectively with experimental scientists and modeling partners in a matrixed R&D environment.
Experience with biologics, antibody/protein sequence models, or protein language models (preferred).
Experience with active learning, Bayesian optimization, sequence-based generative models, MLOps, or knowledge graphs (preferred).
Machine Learning
Python
PyTorch
JAX
Deep Learning
Protein Language Models
Generative Modeling
Representation Learning
Multimodal Learning
Geometric Neural Networks
Active Learning
AlphaFold
Biologics Design
Protein Engineering
Drug Discovery
Python
SQL
Machine Learning
Artificial Intelligence
Large Language Models
Pandas
Scikit-learn
NumPy
API Development
Data Science
Generative AI
A/B Testing
Causal Inference
Data Pipelines
Deep Learning
Time Series Analysis
Business Intelligence
Data Governance