Scientist, Investigative Toxicology

Key Skills

Aartificial intelligence, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Python Programming, R Programming, SQL

Job Description

At the Janssen Pharmaceutical Companies of Johnson & Johnson, what matters most is helping people live full and healthy lives. We focus on treating, curing and preventing some of the most devastating and complex diseases of our time. And we pursue the most promising science, wherever it might be found. 

Janssen Research & Development, LLC discovers and develops innovative medical solutions to address important unmet medical needs in oncology, immunology, neuroscience, infectious diseases and vaccines, cardiovascular and metabolism, and pulmonary hypertension. Please visit http://www.janssenrnd.com for more information.

The Scientist, Investigative Toxicology, will be part of the multi-disciplinary Translational Pharmacokinetics, Pharmacodynamics and Investigative Toxicology team in Nonclinical Safety and will have first-hand involvement throughout the entire life cycle in drug development. This individual will have strong computational skillsets and an established track-record on in-depth analysis from multi-omics research, including but not limited to, (spatial or single cell) transcriptomics, proteomics, or lipidomics.

Principal Responsibilities:

• Provide robust bioinformatic and computational support for (multi)-omics datasets, to aid the development of computational models to explain preclinical tox mechanism and their translational relevance to humans.
• Leverage multimodal data (from areas such as human genetics, pathology, various biochemical or cellular approaches) effectively to guide mechanistic understanding.
• Partner closely and contribute within a multi-disciplinary investigative toxicology team to leverage expertise in biochemistry, cell and molecular biology, and computational chemistry.
• Enable Machine Learning (ML)/Artificial Intelligence (AI) approaches for data-driven toxicology prediction and retrospective analysis by leveraging internal and external data sources.
• Become a pivotal member of the evidence generation group together with nonclinical safety (Pathologists, Toxicologists, Immunologists, and In Vivo Scientists) and nonclinical pharmacokinetics and pharmacodynamics colleagues to aid mechanistic illustration, advancing programs, and regulatory submissions.
• Communicate study results and provide guidance to stakeholders in applying those results toward strategic, scientific, and portfolio decisions.
• Ensure timely delivery of high-quality results.

Qualifications

• A minimum of a Master’s degree in Computational Biology, Computational Toxicology, Data Science, or a related scientific discipline is required. Ph.D. degree in Computational Biology, Computational Toxicology, Data Science, or a related scientific discipline is preferred.
• A minimum of 3 years of post-graduate experience in an academic or industry setting with a Master’s degree or a minimum of 1 year of post-graduate experience in an academic or industry setting with a Ph.D. degree is required.
• Knowledge of technologies used in support of computational research, such as programming languages (R and Python), databases (SQL), version control, and scientific workflow tools, is required.
• Experience with multi-omics data (RNA gene expression, proteomics, epigenomics, genetics, etc.) is required.
• A broad and clear understanding of Biology is required.
• A successful track-record of publications preferred.
• Experience with advanced analytical approaches (Natural Language Processing (NLP), Machine Learning (ML)/Artificial Intelligence (AI), Large Language Model (LLM)) to extract relevant sources of annotation from unstructured data is preferred.
• Knowledge of various in vitro and in vivo sciences preferred.
• Experience interfacing with mass spectrometry or other wet labs techniques preferred.
• Biomarker experience is preferred.
• Experience with clinical research is preferred.
• Experience with real-world evidence is preferred.
• Experience with cloud computing tools preferred.
• Must have excellent verbal and written communication skills.
• Must be detail-oriented with strong organizational skills.
• The ability to work collaboratively with cross-functional scientific teams to deliver on objectives in an integrated fashion within defined timelines is required.
• This position will require minimal travel.