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Quantitative Analyst

Thompson Consulting and Analytics
Remote

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Job Details

Contract
$100 - $150 an hour
1 hour ago

Benefits

Flexible schedule

Qualifications

Statistics
Version control
Data preprocessing
Quantitative applied research
Data Integration (Data management)
Statistics
Econometrics
SAS
R
Master's degree
Data quality management
Econometric analysis
Data integrity and documentation
Attention to detail
Regression analysis
Python
Statistical analysis tools

Full Job Description

Job Overview

We are seeking a dynamic and detail-oriented seeking a Quantitative Analyst to support a federally funded housing policy research project. We have been invited to submit a Phase 2 application for a HUD Housing Policy Research Grant. If awarded, this is an 18-month cooperative agreement beginning October 1, 2026.

The proposed study will employs a quasi-experimental analytical framework integrating panel data regression, two-way fixed effects (TWFE) estimation, and difference-in-differences (DiD) regression analysis applied to a stratified purposive sample of counties. This will be a publication-quality research project with a final deliverable intended for HUD's huduser.gov research library and peer-reviewed journal submission.

Duties

Build and document a balanced multi-source panel dataset at the county level across approximately sampled counties in the study region.
Integrate data from public sources, third-party sources, and directly collected jurisdiction-level permitting records.
Execute propensity score matching procedures.
Maintain full data documentation and version control consistent with HUD replication file standards.
Estimate two-way fixed effects (TWFE) panel regression models for both research questions at county and MSA levels using SAS, R, or Python.
Implement difference-in-differences identification strategy including housing-activity-weighted treatment indicator construction and staggered treatment timing adjustments.
Estimate Callaway-Sant'Anna heterogeneity-robust group-time ATT(g,t) estimators alongside standard TWFE specifications to assess staggered adoption bias.
Execute event study specifications for parallel trends testing across pre-reform and post-reform periods.
Conduct full robustness check program: placebo treatment tests, alternative control group specifications, Chow tests for structural breaks, and sub-group analyses.
Produce clean, formatted output tables and figures for the Preliminary Findings Brief and Policy Action Toolkit .
Support drafting of the final research report methodology, results, and robustness sections under PI's authorship direction.
Prepare fully documented replication files.
Respond to any additional analyses requested during HUD's review and revision cycle for the draft research report.

Required Qualifications

Graduate degree in economics, applied economics, public policy, statistics, or a closely related quantitative field. Doctoral student (ABD), recent Ph.D., or master's degree with substantial applied research experience.
Demonstrated direct experience building and analyzing panel datasets. Ability to describe specific panel data projects including unit of observation, time dimension, and estimation approach.
Working proficiency in at least one of SAS, R, or Python for econometric analysis, including fixed effects panel regression.
Working knowledge of difference-in-differences methods including understanding of the parallel trends assumption, treatment effect identification, and event study specifications.
Ability to produce clean, commented, reproducible analytical code consistent with open science and replication standards.
Strong data management skills including multi-source data integration, geographic identifier matching (FIPS codes, CBSA codes), and data cleaning at scale.
Availability for concentrated engagement during the Phase 2 analytical sprint (Months 7–12, approximately October 2027 – March 2028) with no major competing commitments during that period.

Preferred Qualifications

Prior experience with housing, real estate, or urban economics datasets — ACS microdata, building permit records, HUD data, or commercial real estate data sources (CoreLogic, CoStar, Zillow).
Familiarity with geographic data and spatial aggregation tools (tidycensus in R, Census API, FIPS/CBSA crosswalks).
Exposure to quasi-experimental methods beyond basic DiD — propensity score matching, heterogeneity-robust DiD estimators, synthetic control, regression discontinuity, or instrumental variables.
Familiarity with Callaway and Sant'Anna (2021) or Sun and Abraham (2021) staggered DiD literature is a significant plus.
Prior experience on federally funded research projects, academic research assistantships, or work producing publication-quality analysis and deliverables.
Experience with data acquired through public records requests or direct administrative data collection from government agencies.

Join us if you're passionate about transforming data into impactful insights! This role offers an engaging environment where your analytical skills will shape strategic decisions that influence economic growth worldwide.

Pay: $100.00 - $150.00 per hour

Benefits:

Flexible schedule

Work Location: Remote

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