Postdoctoral Scholar, Machine Learning Applications to Improve Housing Systems

Northern Arizona University • Flagstaff, AZ • 3d ago

This job was posted by https://www.azjobconnection.gov : For more information, please see: https://www.azjobconnection.gov/jobs/7008975

rPostdoctoral Scholar, Machine Learning Applications to Improve Housing Systems

rLocation: Psychology

rRegular/Temporary: Regular

rJob ID: 608349

rFull/Part Time: Full-Time

r Workplace Culture

NAU aims to be the nation\'s preeminent engine of opportunity, vehicle of economic mobility, and driver of social impact by delivering equitable postsecondary value in Arizona and beyond.

{ckesavedhref="https://nau.edu/president/strategic-plan/" isparent="" target="_blank"}

Special Information

This position is an on-site position which requires the incumbent to

complete their work primarily at an NAU site, campus, or facility with or without accommodation. Opportunities for remote work are rare.

The initial appointment is for a duration of 15 months (June 2025 to

August 2026) with the possibility of an extension through May 2027.

This position is posted as **Postdoctoral Scholar, Machine Learning

Applications to Improve Housing Systems** which is a working title. The NAU system title for this position is Postdoctoral Scholar.

This position is subject to the availability of grant funding. The

incumbent is not eligible for Classified Staff layoff or Service Professional recall status.

Job Description

The Arizona Housing Analytics Collaborative (www.azhac.org) is a multidisciplinary team of faculty, students, and staff from Arizona State University, Northern Arizona University, and the University of Arizona, that leverages cutting-edge data analytics and community-based evaluation methods to provide actionable insights into housing and homelessness service delivery systems in the State of Arizona. AzHAC seeks a Postdoctoral Scholar with training in Data Science, Machine Learning, and related fields to support the development and deployment of predictive models/machine learning models aimed at evaluating programs addressing housing insecurity and homelessness. The AzHAC team has access to large-scale administrative data sources collected by state, county, and municipal agencies, and maintains a HIPAA-compliant high-performance computing environment housed at Arizona State University to support these analyses. The initial appointment is for a duration of 15 months (June 2025 to August 2026) with the possibility of an extension through May 2027. The position is funded by a grant from the Garcia Family Foundation and will be housed in the Department of Psychological Sciences at Northern Arizona University (NAU), with additional supervision provided by faculty from the Department of Mathematics and Statistics at NAU.

Coordination with AzHAC Team personnel and Stakeholders to Report Findings - 20%

Coordinate with AzHAC team members and Agency/Community stakeholders

to identify specific criterion and predictor feature attributes

Communicate preliminary findings to stakeholders and develop

collaborative relationships to guide model refinement

Create and distribute presentations, technical documentation, and

manuscripts reporting on model features and findings

Screening, Preparation, and Curation of Raw Data Sources - 20%

Engage in hands-on data preparation and supervise AzHAC Data

Engineers and Data Scientists in the preparation of raw data from multiple administrative and public-use sources

Lead efforts to generate descriptive visualizations of essential

data elements to ensure quality

Formulation, Construction, Evaluation, and Refinement of Models - 60%

Using Stakeholder input, identify candidate models that answer

substantive system and program evaluation questions

Build, evaluate and interpret Predictive Models/Machine Learning

Models to examine identified questions

Iteratively refine existing models in response to Stakeholder

feedback, model testing and use, and the acquisition of new data sources

Minimum Qualifications

PhD or equivalent doctorate (completed by June 1, 2025) in Machine

Learning, Data Science, Predictive Analytics, Statistics, Computer Science, Quantitative Psychology, Sociology, Public Policy, or related fields.

The candidate must have extensive documented experience applying

machine learning and predictive modeling techniques to real-world data.

Preferred Qualifications

PhD or equivalent doctorate (completed by June 1, 2025) in Machine

Learning, Data Science, Predictive Analytics, or Statistics.

Prior experience applying ML/PM to data generated by healthcare,

housing, or government services programs.

Prior experience writing technical reports and dissemination

materials appropriate for a general audience.

Knowledge, Skills, & Abilities

Knowledge

Knowledge of contemporary methods in data science, including methods for continuous outcomes (e.g., spline models) and classificati

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