Open Rank Associate Professor/Professor in Data Science

Rutgers University; Piscataway Township (NJ); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

Medical, prescription drug, and dental coverage

Paid vacation and holidays

Competitive retirement benefits

Employee educational benefits

Life insurance coverage

Employee discount programs

Job summary

Rutgers University seeks an Open Rank Associate Professor/Professor in Data Science to join the Brain Health Institute. This role involves leadership in data analysis related to Alzheimer's disease and mentorship of emerging researchers. The ideal candidate will have a strong background in machine learning and data handling within interdisciplinary teams.

Benefits include comprehensive health coverage, retirement benefits, and educational opportunities for employees and dependents.

Qualifications

• Strong expertise in data analysis and machine learning.
• Experience analyzing diverse data types including clinical, genetic, imaging, and digital data.
• Record of multidisciplinary collaboration and mentorship.

Responsibilities

• Serve as a key leader in developing data-related aspects of a dementia risk cohort.
• Implement machine learning models to improve diagnostic accuracy.
• Lead a program of federally funded research in Alzheimer's disease.

Skills

Advanced statistical and machine learning techniques

Leadership in research programs

Excellent written and oral communication skills

Experience working with underserved communities

Education

Ph.D., MD, MD/PhD, or equivalent terminal degree in Data Science, Bioinformatics, Neuroscience, or a related field

Job description

Open Rank Associate Professor/Professor in Data Science

Position: Open Rank Associate Professor/Professor in Data Science at the Brain Health Institute (BHI), Rutgers Biomedical & Health Sciences (RBHS), located in Piscataway, NJ.

About the Role

We are seeking a faculty member who is experienced in using advanced statistical and machine learning techniques to analyze datasets related to aging, Alzheimer’s disease or related dementias. The successful candidate will work as part of a multi‑disciplinary team including neurologists, neuropsychologists, and neuropathologists. The position involves scientific leadership, mentorship of young investigators, and playing a critical role in advancing research on Alzheimer’s disease, dementia, and related neurodegenerative disorders.

Responsibilities

• Serve as a key leader in developing all data‑related aspects of a well phenotyped dementia risk cohort.
• Provide leadership in ensuring robust data analysis protocols.
• Lead a program of federally funded, data‑driven research in Alzheimer’s disease and related dementias that will contribute to the development of, and leveraging of, the research cohort.
• Lead and collaborate on manuscripts using cohort data.
• Oversee the collection, curation, and management of clinical, genetic, imaging, digital and biomarker data.
• Develop predictive models and algorithms to identify risk factors, disease markers, and potential therapeutic targets for Alzheimer’s disease.
• Implement machine learning models to improve prediction, diagnostic accuracy and monitoring of disease progression.
• Lead and contribute to grant proposals that support future funding of cohort data collection and address research questions of high scientific and public health importance.
• Successfully compete for extramural funding.
• Participate in mentoring, training, and professional development of staff, post‑doctoral fellows, junior physicians, students, and others collaborating on the cohort study.

Qualifications and Eligibility

• Minimum Education: Ph.D., MD, MD/PhD, or equivalent terminal degree in Data Science, Bioinformatics, Neuroscience, or a related field.
• Current Federal Funding: A research program with currently active federal funding. Preference for NIH‑funded investigators at the Associate Professor level or above; superb young investigators with NIH funding and a high potential for independence will also be considered.
• Expertise: Strong expertise in data analysis, machine learning, and experience analyzing diverse data types (clinical, genetic, imaging, digital, blood biomarker).
• Cultural Competence: Experience working with underserved communities is desirable.
• Knowledge & Skills:
  • Advanced statistical and machine learning techniques and interpretation.
  • Leadership in an independent federally funded research program.
  • Understanding of informatics infrastructures supporting high performance computing, cloud computing, and secure computing environments.
  • Experience developing cross‑platform, user‑friendly applications for data processing and analysis.
  • Knowledge of learning healthcare systems and clinical data extraction, transfer, loading, and de‑identification.
  • Academic credentials qualifying for appointment as Associate Professor or Professor (records of successful extramural funding and peer‑reviewed publications).
  • Record of multidisciplinary collaboration and mentorship of students and early‑stage investigators.
  • Excellent written and oral communication skills.

Benefits

• Medical, prescription drug, and dental coverage.
• Paid vacation, holidays, and various leave programs.
• Competitive retirement benefits, including defined contribution plans and voluntary tax‑deferred savings options.
• Employee and dependent educational benefits (when applicable).
• Life insurance coverage.
• Employee discount programs.

Equal Employment Opportunity Statement

It is university policy to provide equal employment opportunity to all its employees and applicants for employment regardless of race, ethnicity, color, national origin, age, ancestry, gender identity or expression, disability status, military service, protected veteran status, sexual orientation, gender expression, or any other category protected by law. For additional information, see the Non‑Discrimination Statement at http://uhr.rutgers.edu/non-discrimination-statement.

Employment Conditions

All offers of employment are contingent upon successful completion of pre‑employment screenings. The University may require proof of influenza vaccination during Flu Season; failure to provide proof may result in rescission of an offer or disciplinary action.