Data Scientist III

Moffitt Cancer Center

1d•$108,493 - $165,422

About The Position

The Data Scientist III will work with stakeholders across Moffitt to document data-related needs and identify novel approaches, including advanced data science and analytics techniques. The role incorporates those approaches into Health Data Services and Digital Innovation strategies and collaborates with clinical and research faculty working in areas such as genomics, radiomics, immuno-oncology, evolutionary therapy, and patient-reported outcomes. The position supports effective use of Moffitt’s data assets by addressing data formatting, lineage, quality, linkages, and visualizations. As new data types are brought into the cloud-based analytics platform, the Data Scientist will contribute to strategies for data provenance, ingestion, linkage, and dissemination. Data types include medical record text, radiology and pathology images, molecular data, and patient-generated data such as wearables. The Data Scientist will implement data and analytics strategies by creating predictive models and algorithms, manipulating datasets to identify trends and extract knowledge, and developing data-mining solutions using natural language processing and machine learning. This work supports clinical applications, including tumor boards, and research such as automation of tumor segmentation.

Requirements

Minimum bachelor's degree in data science, mathematics, statistics, computer science, or a related quantitative field; a master's degree is preferred.
Bachelor’s degree in a quantitative field and more than 5 years of experience in quantitative analytics and applied data science, or a master's or doctorate degree in a quantitative field and more than 3 years of such experience.
Strong programming skills in development of AI/ML, natural language processing, and statistical modeling applications.
Strong proficiency programming in R, Python, or adjacent languages; advanced proficiency in R and experience using Python are listed among the ideal candidate qualifications.
Experience writing SQL queries, mining large datasets using pandas or dplyr, and using data visualization tools such as matplotlib, ggplot2, or plotly.
Advanced expertise in statistical modeling, survival analysis, predictive analytics, machine learning, and analysis of large clinical or registry datasets.
Experience leading complex analyses for peer-reviewed publications, scientific abstracts, and presentations.
Experience serving as a scientific collaborator with investigators and multidisciplinary research teams.
Experience supporting competitive grant applications through statistical analysis plans, study design, sample size calculations, methodology sections, tables, figures, and interpretation of results.
Demonstrated experience contributing to or leading manuscript preparation for peer-reviewed journals.
Experience working with large multi-institutional, registry-based, or real-world healthcare datasets.

Nice To Haves

Master’s degree in a quantitative field is preferred.

Responsibilities

Work with Moffitt stakeholders to document data-related needs and identify advanced data science and analytics approaches.
Incorporate data science approaches into Health Data Services and Digital Innovation strategies.
Collaborate with clinical and research faculty across areas including genomics, radiomics, immuno-oncology, evolutionary therapy, and patient-reported outcomes.
Contribute to strategies for data formatting, lineage, quality, linkages, visualizations, provenance, ingestion, and dissemination.
Create predictive models and algorithms and manipulate datasets to identify trends and extract meaningful knowledge.
Develop data-mining solutions using natural language processing and machine learning to extract structured data.
Support clinical use cases such as tumor boards and research such as automation of tumor segmentation.
Lead complex analyses for peer-reviewed publications, scientific abstracts, and presentations.
Support competitive grant applications through statistical analysis plans, study design, sample size calculations, methodology sections, tables, figures, and interpretation of results.
Contribute to or lead manuscript preparation for peer-reviewed journals.