Founding Geospatial ML Engineer: Forecasting in Production

Homecastr; New York (NY); Hybrid; USD 295,000 - 325,000; Full time

Job summary

Homecastr is seeking a founding Geospatial Machine Learning Engineer to transform our spatial forecasting model into a production-ready system. You will own end-to-end modeling from design to deployment, with a focus on rigor, calibration, and explainability across regional indicators.

The role requires leadership in probabilistic forecasting, geospatial data handling, and reproducible training pipelines, aiming to deliver practical, tested solutions for public-sector and industrial applications.

Qualifications

• Six or more years in applied ML, scientific computing, geospatial modeling, forecasting, or a related field, with staff-level ownership or equivalent evidence.
• Strong Python and modern ML framework experience, including production model development.
• Skill with probabilistic or time-series evaluation, uncertainty, calibration, or comparable statistical rigor.
• Experience with geospatial data, coordinate systems, spatial joins, geographic hierarchies, and large spatial datasets.
• Experience building reproducible training and evaluation systems rather than notebook-only analysis.
• Ability to move between research questions, data engineering, model implementation, and production constraints.
• Clear scientific writing and the judgment to state limitations precisely.

Responsibilities

• Design, train, evaluate, and deploy forecasting models across parcels, buildings, neighborhoods, infrastructure, utilities, and regional indicators.
• Build leakage-resistant historical datasets with explicit vintages, geographic crosswalks, target definitions, and reproducible feature construction.
• Establish benchmarks across places, horizons, baselines, and public planning models.
• Measure point accuracy, probabilistic scores, calibration, coverage, tails, geographic transfer, and failure modes.
• Build uncertainty estimates and explanations that are technically defensible and useful to practitioners.
• Develop and test intervention-conditioned or scenario models without overstating causal identification.
• Own experiment tracking, model lineage, data quality checks, training reproducibility, and model cards.
• Partner with the product engineer to deploy models through stable services with monitoring, cost controls, and rollback capability.
• Work with public-sector practitioners and independent reviewers to turn domain criticism into better datasets, tests, and model behavior.
• Communicate methods and limitations clearly in technical documents, customer materials, and diligence artifacts.

Skills

Python

ML frameworks

Production models

Probabilistic evaluation

Geospatial data

Reproducible training

Tools

GeoPandas

PostGIS

GDAL

xarray

rasterio

Job description

Homecastr is seeking a founding Geospatial Machine Learning Engineer to transform our spatial forecasting model into a production-ready system. You will own end-to-end modeling from design to deployment, with a focus on rigor, calibration, and explainability across regional indicators.

The role requires leadership in probabilistic forecasting, geospatial data handling, and reproducible training pipelines, aiming to deliver practical, tested solutions for public-sector and industrial applications.