Senior Level Nurse Scientist Jobs
Senior level nurse scientist jobs place experienced clinician-researchers in charge of study design, evidence translation, and the multidisciplinary teams that carry scientific findings into practice. Openings are concentrated across Technology & Software, Biotechnology & Pharmaceuticals, and Consulting & Professional Services, with 33% remote or hybrid availability, and employers like Amazon, SentiLink, and St. Jude Children's Research Hospital hiring at this level now.
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Showing 5 of 439+ Senior Level Nurse Scientist jobs
Job Title: Data Scientist 8+ years
Location: New Jersey
Employment Type: Full-Time
About the Role:
We are seeking a highly skilled and experienced Data Scientist to develop and implement advanced machine learning, statistical modeling, predictive analytics, and AI solutions that drive data-driven business decisions.
Key Responsibilities:
- Develop, train, validate, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation, anomaly detection, and predictive analytics use cases.
- Perform exploratory data analysis (EDA), statistical analysis, data profiling, data preprocessing, data cleansing, and feature engineering to identify meaningful patterns and business insights.
- Build predictive models using Python, Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, and other machine learning frameworks.
- Apply advanced statistical modeling, hypothesis testing, regression analysis, probability, time-series analysis, correlation analysis, and experimental design to solve complex business problems.
- Develop and optimize Deep Learning models using frameworks such as TensorFlow, PyTorch, Keras, and related AI/ML technologies.
- Design and implement Natural Language Processing (NLP) solutions for text classification, sentiment analysis, document processing, information extraction, text summarization, and semantic analysis.
- Work with Generative AI, Large Language Models (LLMs), embeddings, vector databases, prompt engineering, Retrieval-Augmented Generation (RAG), and AI-powered applications where applicable.
- Develop end-to-end data science solutions, from data collection and preprocessing through model development, evaluation, deployment, monitoring, and continuous improvement.
- Evaluate model performance using appropriate evaluation metrics, cross-validation, hyperparameter tuning, model selection, explainability, and statistical validation techniques.
- Implement MLOps practices model versioning, experiment tracking, deployment automation, model monitoring, drift detection, and lifecycle management.
- Work with cloud-based AI/ML platforms such as Microsoft Azure, Azure Machine Learning, Azure Databricks, Azure Data Lake, and Azure Synapse Analytics.
- Develop scalable data processing and machine learning workflows using Apache Spark, PySpark, Databricks, and distributed computing technologies.
- Create dashboards, visualizations, and analytical reports using Power BI, Tableau, Matplotlib, Seaborn, or other visualization tools to communicate insights to business stakeholders.
Required Skills & Qualifications:
- 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics.
- Strong programming expertise in Python, with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
- Extensive knowledge of Machine Learning algorithms, including Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, Clustering, PCA, and Ensemble Methods.
- Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and model optimization.
- Experience with Natural Language Processing (NLP), including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models.
- Strong understanding of Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, Vector Databases, Embeddings, and AI application development.
Job Title: Data Scientist 8+ years
Location: New Jersey
Employment Type: Full-Time
About the Role:
We are seeking a highly skilled and experienced Data Scientist to develop and implement advanced machine learning, statistical modeling, predictive analytics, and AI solutions that drive data-driven business decisions.
Key Responsibilities:
- Develop, train, validate, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation, anomaly detection, and predictive analytics use cases.
- Perform exploratory data analysis (EDA), statistical analysis, data profiling, data preprocessing, data cleansing, and feature engineering to identify meaningful patterns and business insights.
- Build predictive models using Python, Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, and other machine learning frameworks.
- Apply advanced statistical modeling, hypothesis testing, regression analysis, probability, time-series analysis, correlation analysis, and experimental design to solve complex business problems.
- Develop and optimize Deep Learning models using frameworks such as TensorFlow, PyTorch, Keras, and related AI/ML technologies.
- Design and implement Natural Language Processing (NLP) solutions for text classification, sentiment analysis, document processing, information extraction, text summarization, and semantic analysis.
- Work with Generative AI, Large Language Models (LLMs), embeddings, vector databases, prompt engineering, Retrieval-Augmented Generation (RAG), and AI-powered applications where applicable.
- Develop end-to-end data science solutions, from data collection and preprocessing through model development, evaluation, deployment, monitoring, and continuous improvement.
- Evaluate model performance using appropriate evaluation metrics, cross-validation, hyperparameter tuning, model selection, explainability, and statistical validation techniques.
- Implement MLOps practices model versioning, experiment tracking, deployment automation, model monitoring, drift detection, and lifecycle management.
- Work with cloud-based AI/ML platforms such as Microsoft Azure, Azure Machine Learning, Azure Databricks, Azure Data Lake, and Azure Synapse Analytics.
- Develop scalable data processing and machine learning workflows using Apache Spark, PySpark, Databricks, and distributed computing technologies.
- Create dashboards, visualizations, and analytical reports using Power BI, Tableau, Matplotlib, Seaborn, or other visualization tools to communicate insights to business stakeholders.
Required Skills & Qualifications:
- 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics.
- Strong programming expertise in Python, with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
- Extensive knowledge of Machine Learning algorithms, including Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, Clustering, PCA, and Ensemble Methods.
- Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and model optimization.
- Experience with Natural Language Processing (NLP), including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models.
- Strong understanding of Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, Vector Databases, Embeddings, and AI application development.
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Find JobsSenior Level Nurse Scientist Job Market
Who's Hiring
- Amazon20
- SentiLink15
- St. Jude Children's Research Hospital14
- Langan Engineering and Environmental Services11
- AbbVie10
Top Industries Hiring
- Technology & Software136
- Biotechnology & Pharmaceuticals63
- Consulting & Professional Services42
- Healthcare & Medical Services30
- Science & Research28
Senior Level Nurse Scientist Jobs: Frequently Asked Questions
How do I get a senior level nurse scientist job?
Employers at this level look for a record of independent research leadership, published or presented work, and demonstrated influence over clinical practice or policy. A doctoral degree is typically expected, and candidates who can show they have mentored junior staff, led multi-site studies, or translated findings into measurable care improvements stand out from the field.
Which companies hire senior level nurse scientists?
Companies hiring senior level nurse scientists right now include Amazon, SentiLink, and St. Jude Children's Research Hospital, based on current listings on Migrate Mate as of November 2026. At this level, hiring tends to come from academic medical centers, large health systems, federal agencies, and research-intensive pharmaceutical or biotech organizations with established nursing science programs.
Are there remote senior level nurse scientist jobs?
Yes, though many roles still require on-site presence for clinical oversight or lab work. About 33% of senior level nurse scientist openings are remote or hybrid as of November 2026, with fully remote positions most common in roles focused on data analysis, protocol development, or health policy research rather than direct care environments.
What makes a nurse scientist role senior level?
Senior level nurse scientist roles are defined by ownership rather than execution. At this stage, professionals lead research agendas, secure or manage grant funding, oversee junior investigators, and are accountable for the scientific quality and practical impact of their work. The expectation shifts from contributing to studies to setting their direction and ensuring findings reach clinical application.
Which industries hire the most senior level nurse scientists?
Senior Level nurse scientist roles concentrate in Technology & Software, Biotechnology & Pharmaceuticals, and Consulting & Professional Services, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they operate large research portfolios, face regulatory pressure to ground care decisions in evidence, and need senior scientists who can bridge the gap between clinical practice and formal research infrastructure.