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Form of workPosition Highlights:
Responsibilities:
Credentials and Qualifications:
- The Data Scientist IV is the second highest level within the Data Scientist career path and is considered a subject matter expert.
- The DS will determine methods and procedures on new assignments and coordinate activities of other personnel.
- The DS will work on complex issues where analysis of situations or data requires in-depth evaluation of variable factors.
- The DS will exercise judgment in selecting methods, techniques, and evaluation criteria for obtaining results.
- The DS will network with key contacts throughout the organization.
- The Data Scientist IV (DS) will work directly with key stakeholders throughout Moffitt to document data-related needs and identify novel approaches, including advanced data science and analytics techniques, for addressing those needs.
- The DS will incorporate these novel approaches into the larger Health Data Services and Digital Innovation strategies.
Responsibilities:
- The DS will liaise directly with both clinical and research faculty whose work is focused on genomics, radiomics, immunooncology, evolutionary therapy, patient-reported outcomes, and other areas to ensure Moffitt's analytics methods are best positioned to accelerate discovery using Moffitt's vast data assets.
- Optimizing the use of Moffitt's data assets requires careful considerations of data formatting, lineage, quality, linkages, and visualizations. As Moffitt continues to incorporate new data types into its cloud-based analytics platform, modern strategies for data provenance, ingestion, linkage, and dissemination will become even more critical for the ultimate realization of the value of these data across the Center. Innovative data types include, but are not limited to, medical record text, radiology and pathology images, molecular data, and patient-generated data (wearables).
- The DS will implement Moffitt's data and analytics strategy through the creation of predictive models and algorithms, manipulation of datasets to identify trends and extract meaningful knowledge, development of data mining solutions using natural language processing and machine learning to extract structured data, and more. Such work will not only have an immediate and high impact on clinical use cases, such as tumor boards, but also on cutting-edge research such as automation of tumor segmentation.
Credentials and Qualifications:
- Master's degree in a quantitative field and 7 years of experience in quantitative analytics and applied data science OR PhD in a quantitative field and 5 years of experience in quantitative analytics and applied data science.
- Experience must include a minimum of 5 years of experience independently leading complex data science projects
