Data Scientist

Emory University; Atlanta (GA); On-site; USD 90,000 - 150,000; Full time

Job summary

The HITI Lab at Emory University in Atlanta, GA is seeking a Data Scientist to support radiology and imaging research by designing clinical imaging data pipelines and developing multimodal AI systems for breast cancer detection and risk assessment. This onsite role focuses on NIH-funded research efforts.

You will blend data engineering, statistics, and machine learning to evaluate new data sources and techniques, collaborating with stakeholders to test and improve clinical outcomes and

Qualifications

• Master’s or PhD in Statistics, Mathematics, Computer Science, or another quantitative field.
• 3 years of relevant experience.
• Expertise in at least one programming language (R, SAS, Python).
• Proficiency with relational databases and SQL.
• Experience with ML methods (clustering, decision trees, neural networks).
• Strong publication or research record.

Responsibilities

• Design and implement clinical imaging data pipelines; build multimodal AI systems for breast cancer detection and risk assessment.
• Validate the performance of commercial clinical AI systems and contribute to NIH-funded research publications.
• Mine and analyze data from internal and external sources to support optimization of operations, clinical outcomes, and business strategies.
• Work with stakeholders to drive data-informed business solutions, using large datasets and predictive models to evaluate different courses of action.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.

Skills

R

SAS

Python

SQL

DICOM

PACS

Education

Master’s or PhD in Statistics/Mathematics/CS

Tools

DICOM

PACS

Job description

The HITI Lab at Emory University is seeking a Data Scientist to support radiology and imaging research through the design of clinical imaging data pipelines and the development of multimodal AI systems. Based in Atlanta, GA and working onsite, this role focuses on building and validating technology for breast cancer detection and risk assessment, within NIH-funded research efforts.

In this position, you will combine data engineering, statistical analysis, and machine learning to evaluate new data sources and techniques, while partnering with stakeholders across the organization to test and improve operational and clinical outcomes.

What you’ll do

• Design and implement clinical imaging data pipelines; build multimodal AI systems for breast cancer detection and risk assessment.
• Validate the performance of commercial clinical AI systems and contribute to NIH-funded research publications.
• Mine and analyze data from internal and external sources to support optimization of operations, clinical outcomes, and business strategies.
• Work with stakeholders to drive data-informed business solutions, using large datasets and predictive models to evaluate different courses of action.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.

What you bring

• Master’s or PhD in Statistics, Mathematics, Computer Science, or another quantitative field, plus 3 years of relevant experience.
• Expertise in at least one programming language (e.g., R, SAS, Python).
• Proficiency in statistical theory and underlying mathematical distributions.
• Ability to query relational databases using SQL.
• Experience with machine learning methods such as clustering, decision tree learning, and artificial neural networks, including practical real-world application.

Tools and technologies

• R
• SAS
• Python
• SQL
• DICOM
• PACS

Preferred qualifications

• Master’s degree in Computer Science, Biomedical Informatics, Data Science, or a related field.
• 2+ years of data science experience in a research or clinical setting.
• Experience with medical imaging data (DICOM, PACS) and AI/ML model development.
• Proficiency in Python and SQL.
• Strong publication or research record.