Alzheimer's Data Scientist - Assistant/Associate/Full Professor

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Position Summary:
The Alzheimer's Data Scientist - Assistant/Associate/ Full Professor will play a pivotal role within FAU's Schmidt College of Medicine in advancing data-driven solutions that have a direct impact on the field of medicine and healthcare, particularly focusing on Alzheimer's disease. This faculty position requires a profound understanding of data science, statistical methods, and healthcare principles. The selected candidate will work collaboratively with researchers, educators, clinicians, and domain experts to design and execute data-driven projects aimed at enhancing healthcare outcomes and deepening our understanding of Alzheimer's disease. The successful candidate will be responsible for leading end-to-end data science and machine learning projects, from initial data exploration to final model deployment, all while adhering to the highest standards of reproducibility, documentation, and code quality.
Summary of Responsibilities:

• Academic Collaboration:
  • Collaborate extensively with interdisciplinary faculty and staff to drive healthcare-related projects, with a special emphasis on advancing our understanding of Alzheimer's disease.
  • Collaborate closely with stakeholders to define clear project objectives, ensuring alignment with the broader goals of the department and the College. Evaluate project feasibility, taking into consideration available resources and timelines.
  • Leverage the collective expertise of the interdisciplinary team to address complex healthcare challenges associated with Alzheimer's disease. Encourage cross-pollination of ideas and methodologies, ultimately leading to holistic and comprehensive solutions.
  • Identify and leverage available resources, including state-of-the-art facilities, cutting-edge technology, and specialized equipment within the College of Medicine. Ensure optimal resource allocation for the successful execution of collaborative projects.
  • Ensure that collaborative projects align with the strategic objectives and mission of the College of Medicine. Contribute to the broader academic community by actively participating in departmental meetings, conferences, and research symposia.

• Project Leadership:
  • Contribute to the formulation of clear project objectives, assessing project feasibility, and defining success criteria in conjunction with stakeholders.
  • Take a lead role in strategic planning for research projects, collaborating with stakeholders to define project scope, objectives, and milestones. Ensure that project goals align with the broader mission of advancing healthcare solutions for Alzheimer's disease.
  • Work closely with the interdisciplinary team to define clear and measurable project objectives. Establish key performance indicators (KPIs) and success criteria to track progress and evaluate the impact of the research.
  • Identify potential risks and challenges associated with project execution. Develop contingency plans and mitigation strategies to address unforeseen obstacles and ensure project continuity.
  • Actively engage in troubleshooting and problem-solving activities, leveraging both technical expertise and collaborative problem-solving skills to overcome obstacles and ensure project success.
  • Maintain comprehensive documentation of project activities, including experimental protocols, data analysis procedures, and outcomes. Generate regular progress reports for stakeholders, highlighting key findings and achievements.

• Independent Research:
  • Independently spearhead projects, offering regular and detailed feedback to fellow team members and stakeholders throughout the project lifecycle.
  • Take the lead in conceptualizing and formulating research initiatives, drawing upon your expertise in data science, statistical methods, and healthcare principles. Define clear research objectives aligned with the broader mission of advancing healthcare solutions for Alzheimer's disease.
  • Develop and refine hypotheses based on a deep understanding of the existing literature, emerging trends, and domain-specific knowledge related to Alzheimer's disease. Ensure that research questions are scientifically rigorous and relevant to the field.
  • Design robust experimental protocols, encompassing data collection methodologies, sampling strategies, and data acquisition techniques. Consider factors such as study population, ethical considerations, and potential confounding variables.
  • Oversee the systematic collection and organization of research data, ensuring data integrity, accuracy, and compliance with ethical guidelines. Implement rigorous data management practices to facilitate subsequent analysis.
  • Conduct comprehensive exploratory data analysis to gain insights into the underlying patterns, trends, and relationships within the research dataset. Employ visualization techniques to communicate findings effectively.
  • Implement rigorous evaluation metrics and validation techniques to assess the performance of machine learning models. Conduct thorough model validation, considering aspects such as overfitting, bias-variance tradeoff, and model stability.
  • Analyze model predictions and research findings in the context of Alzheimer's disease, offering meaningful interpretations and insights. Consider the clinical implications and potential applications of the research outcomes.

• Data Science Proficiency:
  • Execute comprehensive data science and machine learning tasks, including exploratory data analysis, data visualization, data preprocessing, model training, and rigorous model evaluation.
  • Apply advanced statistical techniques and methodologies to analyze complex healthcare datasets related to Alzheimer's disease. Utilize inferential statistics, regression analysis, and hypothesis testing to uncover meaningful insights.
  • Leverage machine learning algorithms to model and predict disease progression, patient outcomes, and other relevant healthcare metrics. Implement techniques such as supervised and unsupervised learning, ensemble methods, and deep learning where appropriate.
  • Ensure that machine learning models are interpretable, providing meaningful insights into the factors influencing Alzheimer's disease. Utilize techniques like feature importance analysis and model explainability methods.

• Software Development Practices:
  • Craft rigorous and reproducible experiments using well-established software development practices, including version control, automated testing, and comprehensive documentation.
  • Maintain a structured and well-documented version history to track changes, facilitate collaboration, and ensure reproducibility.
  • Implement robust error handling procedures to gracefully manage exceptions and unexpected behaviors. Utilize logging mechanisms to capture critical information for debugging and performance monitoring.
  • Identify and address performance bottlenecks in code and algorithms. Apply optimization techniques to enhance computational efficiency and responsiveness.

• Code Development and Presentation:
  • Develop both low-level code and code to present findings to stakeholders using tools such as Jupyter notebooks, Streamlit, or equivalent platforms.
  • Design code with a modular structure, emphasizing reusable components and clear function separation. Apply design patterns and best practices to enhance code maintainability and facilitate future updates and extensions.
  • Develop compelling and informative presentations to communicate research outcomes to a diverse audience, including colleagues, stakeholders, and potentially non-technical stakeholders. Craft presentations that effectively convey technical intricacies and domain-specific nuances.
  • Use code and visualizations to craft a compelling narrative that guides stakeholders through the research process, from data exploration to final insights. Ensure that the story is logical, coherent, and easily digestible.

• Knowledge Dissemination:
  • Effectively communicate the outcomes of experiments to a diverse audience, adeptly elucidating both technical intricacies and domain-specific nuances.
  • Collaborate with colleagues to design and develop educational materials and curricula related to data science, machine learning, and their applications in healthcare. Ensure that content is structured, comprehensive, and aligns with the learning objectives.
  • Provide mentorship and guidance to students, researchers, and junior faculty members who are exploring data science and machine learning. Offer support in the form of advice, resources, and feedback on projects and research endeavors.

• Training: Contribute significantly to the expansion of the organization's data science and machine learning knowledge by conducting training sessions for a wide range of personnel.

• Stay Informed: Stay abreast of emerging developments in data science and machine learning, specifically in relation to healthcare applications.

• Publication Support: Provide essential support for the publication of experiment outcomes in peer-reviewed scientific papers, encompassing the creation of textual content, graphical representations, and tables for the technical sections of these publications.

• Fulfill additional responsibilities as necessitated by the dynamic nature of the role.

The College of Medicine at Florida Atlantic University seeks scholars who are committed to supporting the learning needs of students from diverse backgrounds and to thoughtfully engaging all communities in higher education. Florida Atlantic University embodies a culture of strategic and collaborative community engagement that results in mutual benefit to the institution and the various internal and external communities that it serves.
Minimum Qualifications:
• Preferred experience within the healthcare industry, demonstrating familiarity with healthcare datasets and concepts.
• PhD in Computer Science, Artificial Intelligence, or a related field, with a focus on data science, data analytics, and/or machine learning.
• Proficiency in data science programming languages such as Python, R, PowerBi, Tableau, or equivalent.
• Familiarity with data science and machine learning frameworks including, but not limited to, Pandas, NumPy, scikit-learn, TensorFlow, PyTorch, Matplotlib, mlr3, caret, dplyr, tidyr, ggplot2, and ggprah.
• Strong grasp of machine learning models and concepts encompassing regression, deep neural networks, transfer learning, and their application to both tabular data and images.
• Proficiency in data visualization tools like Jupyter notebooks or Streamlit.
• Experience working with version control tools such as Git and GitHub.
• Demonstrated capability in producing production-quality code, encompassing comprehensive documentation, dependency management, and automated testing.
• Exceptional communication skills, both written and verbal, with an ability to convey complex concepts to diverse audiences.
• Proven ability to independently strategize and execute intricate projects, including analyzing and documenting complex datasets.
• Proficiency in analyzing and documenting complex datasets.
• Familiarity with open-source tools and cloud environments.
• Knowledge of contemporary machine learning trends and their applicability to healthcare (preferred).
• Familiarity with healthcare databases (preferred).
• Understanding of healthcare concepts including DICOM, EHR, and FHIR (preferred).
• Demonstrated involvement in scholarly publishing, evidenced by authored papers in peer-reviewed journals (preferred).
Salary:
Commensurate with experience
College or Department:
College of Medicine
Location:
Boca Raton
Work Days and Hours:
N/A
Application Deadline:
2024-01-01
Special Instructions to Applicant:
This position is open until filled and may close without prior notice.
This position is subject to funding.
All applicants must apply electronically to the currently posted position on the Office of Human Resources' job website (https://fau.edu/jobs) by completing the required employment application for this recruitment and submitting the related documents.
Required Documents:
FAU's Career Page permits the attachment of required/requested documentation.
PLEASE NOTE: A maximum of five (5) documents may be attached to your application. If more than five (5) documents are required for submission, please combine additional documents into a single attachment to not exceed the maximum permitted.
The site permits the attachment of required/requested documentation. When completing the online application, please upload the following: (i) a cover letter, (ii) curriculum vitae, and (iii) samples of your previous work and publications.
Transcripts:
The selected candidate for this position is required to submit an official transcript sent directly from the institution for the Provost's credential file prior to the first day of employment. Similarly, for degrees from outside the United States, the evaluation by an organization belonging to the National Association of Credential Evaluation (NACES), with an indication of the documents the evaluation was prepared from (official transcripts, diplomas, dissertation abstracts) is required for the Provost's credential file prior to the first day of employment. Transcripts must be issued to Florida Atlantic University, not to the individual applicant.
Background Screening:
Selected candidates must successfully complete and pass all employment screenings prior to the start of employment. Employment screenings may include a criminal background check (level I and level II), motor vehicle check, credit check, reference checks, alcohol, and drug screening check.
As a Hispanic-serving institution, Florida Atlantic University (FAU) is one of the most racially and ethnically diverse institutions in the Florida state university system. We are committed to recruiting and retain...