Senior Level Clinical Data Coordinator Jobs
Senior level clinical data coordinator jobs put experienced professionals in charge of data integrity strategy, cross-functional study oversight, and the clinical operations teams that keep trials on track. Opportunities are concentrated across Technology & Software, Consulting & Professional Services, and Banking & Financial Services, with 41% of openings offered remotely or hybrid, and employers like Capital One, Oracle, and JPMorganChase hiring at this level now.
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Showing 5 of 879+ Senior Level Clinical Data Coordinator 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 Clinical Data Coordinator Job Market
Who's Hiring
- Capital One27
- Oracle15
- JPMorganChase14
- Meta13
- Amazon13
Top Industries Hiring
- Technology & Software324
- Consulting & Professional Services88
- Banking & Financial Services58
- Construction & Real Estate37
- Investment & Asset Management35
Senior Level Clinical Data Coordinator Jobs: Frequently Asked Questions
How do I get a senior level clinical data coordinator job?
Employers hiring at the senior level look for candidates who have led data management activities end-to-end on complex multi-site or global studies. Demonstrating direct experience with CDASH and CDISC standards, proficiency in EDC systems like Medidata Rave or Oracle Clinical, and evidence of mentoring junior coordinators or reviewing peers' work will distinguish you from mid-level applicants. A portfolio of database lock contributions and audit-ready documentation strengthens any application significantly.
Which companies hire senior level clinical data coordinators?
Companies hiring senior level clinical data coordinators right now include Capital One, Oracle, and JPMorganChase, based on current listings on Migrate Mate as of November 2026. At this level, hiring tends to come from large pharmaceutical sponsors, full-service contract research organizations, and biotech firms running late-phase or commercial-stage clinical programs.
Are there remote senior level clinical data coordinator jobs?
Yes, remote and hybrid availability at the senior level is substantial in this field. About 41% of senior level clinical data coordinator openings are remote or hybrid as of November 2026, reflecting how established data management workflows and enterprise EDC platforms have made location-independent oversight practical for experienced professionals managing study data across distributed site networks.
What makes a clinical data coordinator role senior level?
Senior level clinical data coordinator roles are distinguished by scope, ownership, and depth. Rather than executing tasks assigned by others, senior coordinators own data management plans, lead database build and validation activities, resolve complex data discrepancies without escalation, and serve as the primary data point of contact for sponsors or investigative sites. Mentoring junior staff and contributing to process improvement or SOPs typically come with the title as well.
Which industries hire the most senior level clinical data coordinators?
Senior level clinical data coordinator roles concentrate in Technology & Software, Consulting & Professional Services, and Banking & Financial Services, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they run the largest volumes of interventional trials, require rigorous regulatory-grade data quality standards, and depend on experienced coordinators to protect data integrity through study completion and submission.