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$130,422–$214,500.
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.
The Sr Scientist Biostatistics joins the Discovery and Manufacturing Statistics organization to develop trustworthy, validated, and reusable AI-enabled statistical solutions for pharmaceutical research and manufacturing. This role operates at the intersection of biostatistics, artificial intelligence, and scientific software development. The successful candidate will build Python services, connect R and Python tools through APIs and Model Context Protocol, and design agentic workflows using large language models, retrieval-augmented generation, and knowledge graphs. Key responsibilities include ensuring statistical integrity, managing uncertainty, and designing end-to-end architectures for production scientific AI applications. Required skills include proficiency in R and Python, statistical inference, predictive modeling, and experience with generative AI. The role solves the challenge of integrating scientific intelligence into digital environments while maintaining reproducibility, traceability, and expert oversight in a regulated, validation-sensitive environment.
Build Python services and connect approved R and Python tools through APIs and Model Context Protocol.
Ensure the statistical integrity of AI-enabled solutions by accounting for variability, uncertainty, and confounding factors.
Design and deploy AI-enabled scientific applications that integrate machine learning, generative AI, and domain knowledge.
Develop agentic workflows to retrieve evidence, invoke scientific models, and support experimental interpretation.
Curate and contextualize scientific data for AI applications while ensuring data quality, provenance, and metadata integrity.
Design end-to-end architectures for production scientific AI applications including user interfaces, APIs, and data flows.
Test applications to evaluate retrieval, tool use, and generated outputs with domain experts to assess scientific validity.
Mentor statisticians and scientists on the effective application of AI, machine learning, and modern computational methods.
What we're looking for
PhD in Statistics, Biostatistics, Machine Learning, Data Science, Computer Science, AI, or a related quantitative discipline, or a Master’s degree with commensurate relevant experience.
Candidates with a primary degree in Computer Science, AI, or Software Engineering must demonstrate advanced knowledge of statistical modeling, study design, uncertainty quantification, and model validation.
Proficiency in R and/or Python for statistical analysis and building maintainable APIs and services.
Strong foundations in statistical inference, predictive modeling, study/experimental design, and experience assessing uncertainty, bias, and model validation.
Hands-on experience developing AI/ML solutions, including generative AI, large language models, retrieval-augmented generation, or agentic workflows.
Demonstrated experience designing end-to-end architectures for production applications, including data flows, API contracts, security, scalability, and reliability.
Experience building modular, tested, and reproducible analytical software using Git and working knowledge of SQL and data pipelines.
Experience in pharmaceutical, biotechnology, or healthcare environments (preferred).
Generative AI
Machine Learning
Natural Language Processing
Large Language Models
Python
SQL
LangChain
DSPy
MLOps
Data Science
Multi-objective Optimization
Real-World Data
Data Visualization
Business Intelligence
Database Management
R
Python
SAS
Julia
R-shiny
Machine Learning
Deep Learning
Bayesian Methods
Statistical Modeling
Simulations
Scientific Computing
FACTS
EAST
Adaptive Designs
Predictive Modeling
SQL
Python
R
SAS
Epic
Clarity
Caboodle
Cogito
FHIR
HL7
CCDA
OMOP
SNOMED CT
LOINC
RxNorm
Tableau
Power BI
Data Science
Biostatistics
Clinical Informatics
Machine Learning
Deep Learning
Natural Language Processing
LLMs
Embedding Models
Rerankers
Distributed Training
Information Retrieval
Neural Networks
RAG
Semantic Search
Generative AI
Machine Learning
Deep Learning
Computer Vision
Computer Graphics
Natural Language Processing
Diffusion Models
Large Language Models
Multimodal Foundation Models
Video Generation
Video Editing