Data Scientist

About This Role

About the Role

The Data Scientist will support CCAD’s academic and clinical research mission by designing, developing, and operationalizing advanced analytics, machine learning, and AI models for investigator-initiated and sponsored research. This role will work closely with clinicians, researchers, and external academic partners to enable data-driven discovery, AI-enabled clinical studies, research registries, and translational innovation. The role will also support evaluation and implementation of external AI research platforms and contribute to CCAD’s AI research infrastructure.

This role will involve designing, developing, and validating AI/ML and statistical models to support investigator-initiated trials, registries, and translational research studies.

Responsibilities

• Design, develop, train, and validate AI/ML and statistical models to support investigator-initiated trials, registries, and translational research studies.
• Support AI/ML model development using structured and unstructured clinical data, imaging, genomics, and real-world data sources.
• Maintain model performance post deployment and retrain or tune the model as required.
• Support retrospective and prospective AI studies, including feasibility, validation, and performance evaluation.
• Collaborate with investigators, research personnel, and research administration to translate clinical and scientific questions into AI/ML-enabled research use cases.
• Contribute to the development and maintenance of research registries.
• Extract, process, clean, and validate quality datasets that might be used in the development of models.
• Collaborate with external partners on joint AI research projects, including data preparation, model development, and publication support.
• Provide technical evaluation and research input on external AI platforms.
• Support preparation of grant applications, protocols, statistical analysis plans, and research deliverables involving AI/ML methodologies.
• Communicate analytical findings clearly to clinical, academic, and executive stakeholders, including limitations, assumptions, and validation approaches.
• Perform other research-related analytical duties as assigned.

Qualifications

• Bachelors in a relevant field such as Statistics, Computer Science, Biomedical Informatics, Data Science, Mathematics, Physics.
• Masters in a relevant field such as Statistics, Computer Science, Biomedical Informatics, Data Science, Mathematics, Physics.

Required Skills

• Strong foundation in statistics, machine learning, and applied AI within a healthcare or biomedical research context.
• Demonstrated experience developing AI/ML models for research, including cohort-based and hypothesis-driven analyses.
• Experience working with clinical data sources (e.g., EMR-derived datasets, registries, imaging, omics, real-world data).
• Experience with transformation and cleaning of both structured and unstructured data.
• Ability to design reproducible research workflows and document analytical methods suitable for publication and regulatory scrutiny.
• Experience with feature engineering, model validation, bias assessment, and performance evaluation in clinical research settings.
• Proficiency in Python and relevant ML/AI libraries (e.g., pandas, NumPy, scikit-learn, PyTorch, TensorFlow).
• Experience with SQL and large-scale data environments; exposure to GPU-enabled computing environments is strongly preferred.
• Experience with extracting data from database management systems.
• Experience with deploying models into production.
• Ability to collaborate effectively with clinicians, researchers, and external academic partners.
• Strong written and verbal communication skills, with the ability to explain complex analytical concepts to non-technical stakeholders.

Preferred Skills

• Experience with the following software:
• Cloud Platforms: Azure, AWS, GCP
• Database Management Systems: MS SQL Server, Azure Data Lake, Azure Synapse
• Data Wrangling: MS SSIS, Azure Data Factory
• Statistical Analysis Software: SAS, SPSS, Minitab
• Version Control: GitHub
• Platform: Databricks, Azure ML
• Scripting Language: Python (Pandas, NumPy, Seaborn, Matplotlib)
• Query Language: SQL
• Statistical Computing Package: R
• Other Languages: Spark SQL
• Deployment Software: Docker
• Visual Analytics: Power BI, Tableau, Minitab, Excel
• UI Tools: Shiny, Power Apps
• Hugging Face / Transformers
• XGBoost / LightGBM
• Epic Clarity / Caboodle
• Healthcare or life sciences research experience is preferred.
• Prior experience supporting grant-funded research, publications, or academic collaborations is highly desirable.