Postdoctoral Scholar, Machine Learning Applications to Improve Housing Systems

Postdoctoral Scholar, Machine Learning Applications to Improve Housing Systems Location:

Psychology Regular/Temporary:

Regular Job ID:

608349 Full/Part Time:

Full-Time 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. 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. 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 classification algorithms (e.g., decision trees, random forests, SVMs, KNN algorithms, etc.), ensemble learning techniques, data reduction and feature selection methods, neural network and deep learning models, and other related tools Knowledge of best practices when applying data science tools to support the evaluation of human services programs and the implications for policy and practice Knowledge of contemporary coding languages and environments used to deploy and evaluate predictive models (i.e., Python, R). Knowledge of general-purpose statistical programming languages (i.e., SAS, Stata) or data visualization tools (e.g., Tableau, Shiny). Skills General organizational and project management skills Can develop and maintain a thoroughly documented code base that is based on a transparent and reproducible architecture Advanced proficiency in modern data science coding languages and environments Abilities Efficiently identifies analytical project goals, constructs a timeline, and progresses through objectives with minimal oversight Communicates frequently and effectively with AzHAC team members and external stakeholders regarding project progress, and is self-directed in the resolution of barriers to progress Articulates analytic processes and findings with sufficient detail and accessibility to effectively solicit and accommodate feedback from collaborators and stakeholders with technical and non-technical backgrounds Equal Employment Opportunity Equal Opportunity Employer, including Disabled/Protected Veterans. NAU is responsive to the needs of dual career couples.

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