Scientific Analyst II

Overview

Center for Innovation in Brain Science (CIBS) at the University of Arizona is seeking a Scientific Analyst II to support data science research focused on neurodegenerative diseases, including Alzheimer's Disease (AD), Parkinson's Disease (PD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). The analyst will work with large-scale biomedical datasets (e.g., UK Biobank, All of Us, Insight, electronic medical records) to investigate the role of menopausal hormone therapy (MHT) and menopause on brain health and identify drug repurposing candidates for prevention and treatment.

This position requires advanced expertise in data science, artificial intelligence, and machine learning to develop, apply, and interpret analytical pipelines integrating multi‑modal clinical, genomic, and epidemiological data.

• Data Analysis and Machine Learning Pipeline Development: Collaborate under moderate guidance to design, develop, and execute machine learning and AI-driven analytical pipelines; apply supervised and unsupervised machine learning algorithms (e.g., logistic regression, random forests, deep learning) to identify risk factors, biomarkers, and patterns associated with neurodegenerative diseases and the effects of MHT on brain health.
• Drug Repurposing Research and Bioinformatics Analysis: Conduct computational drug repurposing analyses to identify existing FDA‑approved compounds with potential efficacy for AD, PD, MS, and ALS prevention and treatment; integrate multi‑omics data with clinical outcomes to prioritize drug candidates; collaborate with wet lab and clinical teams for translational interpretation.
• Epidemiological and Clinical Data Management and Harmonization: Access, curate, harmonize, and manage large population‑based datasets; ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols; develop and maintain reproducible data pipelines using Python, R, and high‑performance computing; perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.
• Scientific Communication, Dissemination, and
  
  Collaboration:
  
  Contribute to peer‑reviewed manuscripts, conference presentations, and grant applications; present results to interdisciplinary teams, departmental seminars, and external stakeholders; maintain documentation of analytical methods to ensure transparency and reproducibility; participate in lab meetings, journal clubs, and professional development activities.
• Research Infrastructure and Continuous Improvement: Maintain and improve lab computational infrastructure, including code repositories (Git Hub) and documentation standards; evaluate and adopt emerging AI/ML tools and methodologies; assist in training junior lab members or graduate students on data science methods.
• Knowledge, Skills and Abilities: Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling; proficiency in Python/R with ML libraries; data wrangling skills for large heterogeneous datasets; SQL and database management knowledge; collaborative skills within interdisciplinary teams; project management and deadline adherence; ability to communicate complex analytical results to varied audiences.

• Master's degree in Data Science, Biostatistics, Bioinformatics, Computational Biology, Computer Science, or related field.
• Minimum of 3 years of relevant work experience.

• Experience with UK Biobank, All of Us Research Program, or similar population cohorts.
• Background in neurodegenerative disease research or women's health.
• Experience with electronic medical records data analysis.
• Experience with version control and reproducible research workflow.

• Health, dental, and vision insurance plans.
• Life insurance and disability programs.
• Paid vacation, sick leave, and holidays.
• U of A / ASU / NAU tuition reduction for employees and qualified family members.
• Retirement plans.
• Access to U of A recreation and cultural activities.

Salary: $59,404 – $74,254 per year. Position is full‑time (1.0 FTE) with 40 hours per week.