below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$133,846–$199,175.
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
56 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 43 roles with salary data.
The Senior Data Scientist - R&D DDSAI - Small Molecule Discovery joins the Data Science team to support the Small Molecule Discovery organization. This role serves as a technical bridge between scientific workflows and modern data platforms, transforming complex molecular, synthesis, purification, and assay data into trusted, AI-ready assets. You will design and develop data products, analytical solutions, and scalable workflows to support the Design-Make-Test-Analyze cycle. Key responsibilities include establishing data standards, metadata practices, and quality frameworks to enable machine learning and scientific AI initiatives. Required expertise includes a Master's or Ph.D. in a relevant scientific or computational discipline, 5+ years of industry experience, and proficiency in Python, SQL, Databricks, Snowflake, and RDKit. You will collaborate with medicinal chemists and automation scientists to accelerate discovery outcomes.
What does a Data Scientist earn?
Median $173200 from 344 postings across 66 companies.
Design and maintain high-quality data assets to support small-molecule discovery workflows.
Transform molecular, synthesis, and assay data into reusable datasets for machine learning and scientific decision-making.
Develop scalable analytical workflows using modern cloud data platforms and scientific computing tools.
Structure and document scientific data to ensure it is AI-ready and meets data quality standards.
Translate scientific requirements from medicinal chemists and automation scientists into computational data solutions.
Create model-ready datasets to support scientific AI initiatives and advanced analytics.
Mentor colleagues on modern data science approaches and scientific data management practices.
What we're looking for
Master's degree or Ph.D. in Chemistry, Cheminformatics, Data Science, Computer Science, Pharmaceutical Sciences, Chemical Engineering, or a related scientific discipline.
5+ years of industry experience in pharmaceutical, biotechnology, or life sciences research environments.
Strong understanding of small-molecule discovery workflows, including molecular structures, reaction data, synthesis workflows, and structure-activity relationships.
Advanced proficiency in Python and SQL.
Experience developing solutions on modern cloud data platforms, including Databricks and Snowflake.
Significant experience working with molecular and reaction data using scientific software libraries and toolkits such as RDKit.
Experience designing analytical workflows and data models for complex scientific datasets.
Understanding of data engineering concepts including data modeling, metadata management, lineage, reproducibility, and data quality.
Python
R
SQL
Machine Learning
Data Engineering
AWS
Snowflake
Redshift
Workflow Orchestration
Version Control
Predictive Modeling
Data Visualization
Business Intelligence
Ontologies
CDISC
SEND
Python
Scikit-learn
Pandas
NumPy
PyTorch
TensorFlow
AWS
Snowflake
Machine Learning
AI
Statistical Modeling
Version Control
Data Visualization
Bayesian Methods
Experimental Design
Business Intelligence
Data Science
Scientific Computing
Python
Mathematical Optimization
Mixed-Integer Programming
Simulation
Monte Carlo
Forecasting
Uncertainty Quantification
Stochastic Programming
Bayesian Decision Methods
Reinforcement Learning
AI/ML
Data Science
Version Control
Data Integration
Digital Twins
Machine Learning
Generative AI
Python
SQL
Natural Language Processing
Large Language Models
Statistical Analysis
Data Science
R
Agentic Workflows
Retrieval
Algorithms
Human-in-the-loop
Metadata Extraction