Scientific Analyst II

Scientific Analyst II

Posting Number
req25756


Department
UAHS Brain Science


Department Website Link

Location
Tucson Campus


Address
Tucson, AZ USA


Position Highlights


The 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, including UK Biobank, All of Us, Insight, and electronic medical records, to investigate the role of menopausal hormone therapy (MHT) and menopause on brain health, and to identify and evaluate drug repurposing candidates for neurodegenerative disease prevention and treatment. The position requires advanced expertise in data science, artificial intelligence, and machine learning to develop, apply, and interpret analytical pipelines that integrate multi-modal clinical, genomic, and epidemiological data. This role directly contributes to the lab's mission of translating large-scale data insights into actionable strategies for the prevention and treatment of neurodegenerative conditions. The primary deliverables of this role are computational pipelines, ML models, and exploratory data outputs.


The Center for Innovation in Brain Science is an "all brains on deck" research environment designed for highly-integrated, collaborative research through innovative team science. With expertise spanning discovery, translational and clinical science, we are addressing complex issues across four age-associated neurodegenerative diseases. Bringing expertise in Alzheimer's, Parkinson's, Multiple Sclerosis and ALS, aging, bioenergetics of the brain, immunology, stem cell biology, big data computational science, animal models of neurodegenerative disease, drug design and synthesis, FDA regulatory and toxicology requirements and clinical trial design and conduct.

This position is funded through research grants. Continuation of the position is contingent upon availability of funding. The successful candidate will join a dynamic, interdisciplinary team at the Center for Innovation in Brain Science (CIBS), working at the forefront of computational neuroscience and population health research. The analyst will have opportunities to contribute to high-impact publications, grant applications, and collaborative multi-site research projects.
This position offers a hybrid work arrangement, combining on-site work at the University of Arizona campus with remote work flexibility.
Outstanding U of A benefits include 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 the employee and qualified family members; retirement plans; access to U of A recreation and cultural activities; and more!

The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocations services, please click here .

Duties & Responsibilities

Data Analysis and Machine Learning Pipeline Development:

• Under moderate guidance collaborate in the design, develop, and execution of machine learning and AI-driven analytical pipelines to analyze large-scale biomedical datasets from UK Biobank, All of Us, Insight, and electronic medical records.
• 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 menopausal hormone therapy (MHT) on brain health.
• Collaborate on the development and validation of predictive models integrating genomic, clinical, lifestyle, and imaging data using general knowledge of principals, theories and concepts.

Drug Repurposing Research and Bioinformatics Analysis:

• Collaborating in 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 (genomics, transcriptomics, proteomics) with clinical outcomes data to prioritize drug candidates.
• Collaborate with wet lab and clinical teams to support translational interpretation of findings.

Epidemiological and Clinical Data Management and Harmonization:

• Access, curate, harmonize, and manage large population-based datasets including UK Biobank, All of Us, and institutional EMR data.
• Ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols.
• Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer.
• Perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.

Scientific Communication, Dissemination, and Collaboration:

• Compare and contribute to peer-reviewed manuscripts, conference presentations, and grant applications reporting research findings on MHT, menopause, and neurodegenerative disease.
• Present results to interdisciplinary research teams, departmental seminars, and external stakeholders.
• Collaborate closely with Dr. Francesca Vitali, co-investigators, and consortium partners. Maintain thorough 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 (GitHub), analytical workflows, and documentation standards.
• Evaluate and adopt emerging AI/ML tools and methodologies relevant to brain science research.
• Assist in training junior lab members or graduate students on data science methods and tools as needed.
• Stay current with literature in neurodegenerative disease, computational.

Knowledge, Skills and Abilities:

• Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling.
• Strong data wrangling and preprocessing skills for large, heterogeneous datasets.
• Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
• Knowledge of drug repurposing methodologies or network pharmacology.
• Knowledge and familiarity with electronic medical records data analysis.
• Knowledge and proficiency with SQL and database management.
• Ability to collaborate effectively within interdisciplinary teams spanning data science, neuroscience, clinical research, and epidemiology.
• Ability to manage multiple concurrent projects and meet deadlines.
• Ability to critically evaluate scientific literature and translate findings into research hypotheses and analytical strategies.
• Ability to communicate complex analytical results clearly to both technical and non-technical audiences.

This job posting reflects the general nature and level of work expected of the selected candidate(s). It is not intended to be an exhaustive list of all duties and responsibilities. The institution reserves the right to amend or update this description as organizational priorities and institutional needs evolve.

Minimum Qualifications

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

Preferred Qualifications

• 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.

FLSA
Exempt


Full Time/Part Time
Full Time


Number of Hours Worked per Week
40


Job FTE
1


Work Calendar
Fiscal


Job Category
Research


Benefits Eligible
Yes - Full Benefits


Rate of Pay
$59,404 - $74,254


Compensation Type
salary at 1.0 full-time equivalency (FTE)


Grade
8


Compensation Guidance
The Rate of Pay Field represents the University of Arizona's good faith and reasonable estimate of the range of possible compensation at the time of posting. The University considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate's work experience, education/training, key skills, and internal equity.


The Grade Range represent a full range of career compensation growth over time. The university offers compensation growth opportunities within its career architecture. To learn more about compensation, please review our Applicant Compensation Guide and our Total Rewards Calculator.


Career Stream and Level
PC2


Job Family
Research & Data Analysis


Job Function
Research


Type of criminal background check required:
Name-based criminal background check (non-security sensitive)


Number of Vacancies
1


Target Hire Date

Expected End Date

Contact Information for Candidates
Francesca Vitali I View email address on click.appcast.io


Open Date
4/21/2026


Open Until Filled
Yes


Documents Needed to Apply
Resume and Cover Letter


Special Instructions to Applicant

Notice of Availability of the Annual Security and Fire Safety Report
In compliance with the Jeanne Clery Campus Safety Act (Clery Act), each year the University of Arizona releases an Annual Security Report (ASR) for each of the University's campuses.Thesereports disclose information including Clery crime statistics for the previous three calendar years and policies, procedures, and programs the University uses to keep students and employees safe, including how to report crimes or other emergencies and resources for crime victims. As a campus with residential housing facilities, the Main Campus ASR also includes a combined Annual Fire Safety report with information on fire statistics and fire safety systems, policies, and procedures.
Paper copies of the Reports can be obtained by contacting the University Compliance Office at View email address on click.appcast.io.