The Role
Develop and maintain product-focused data science and machine learning models for land-use suitability, land valuation, comparable transactions, and community sentiment. Source and curate geospatial and market datasets, design evaluation frameworks, improve existing models, and collaborate with design, product, and engineering to deliver customer-facing insights. The role requires production-quality Python, SQL, geospatial tooling, reproducible workflows, and the ability to explain analytical outputs to non-experts.
Summary Generated by Built In
About Us
The Role
What You'll Do
What We're Looking For
Additional Nice to Haves
Working at Aarden
Compensation
Aarden is a land intelligence platform that helps landowners, investors, and developers figure out what a piece of land can actually be used for, and how to market it. We turn messy parcel, infrastructure, market, community, and ecological data into clear, bankable answers for land-dependent assets. Our goal is to become the default decision layer for land: helping physical projects start in places where they can be built and supported for decades.
Aarden’s business is built around providing unique, highly trusted insights about land use, land value, and market dynamics to our customers. We are looking for a product-focused data scientist to help us maintain and develop several of the models and analytics that power those insights.
Today that work spans three primary areas. First, we score parcel-level suitability for specific development uses, so a given site gets assessed differently depending on whether someone is evaluating it for conservation, a data center, solar, wind, or residential development. Second, we run an automated valuation model and comparable transactions identification for bare land, where comps are often thin and estimating the value of land is contingent on how the land might be used. And third, we model current and historic community sentiment related to different development activities. You'd own meaningful pieces of these modeling efforts and help decide what analytical capabilities we build next.
- Build new models and analytics that surface insights from our ever-growing database of land and market attributes.
- Extend and refine existing models to improve performance and support new use cases.
- Source, evaluate, and curate datasets on land use, property characteristics, and market factors, both as model inputs and for validation.
- Work with design, product, and engineering to deliver your work to customers. If you're interested, you'll also have the chance to build user-facing features yourself.
- Strong data science and ML fundamentals, plus hands-on experience with geospatial data and tooling (GDAL/OGR, Postgres/PostGIS, Sedona, or similar).
- Production-quality Python and fluent SQL, with willingness to pick up other languages as needed (e.g., TypeScript).
- A track record of designing evaluation frameworks for geospatial, environmental, or socioeconomic models, where ground truth may be scarce, imbalanced, or contested.
- Experience using LLMs as components in data pipelines or modeling.
- Proven discipline for building data curation and model development workflows that are documented, reproducible, and versioned.
- Good judgment about how your outputs impacts customers, including the ability to explain them to non-experts.
- Experience in one or more of the domains we model: renewable energy, land conservation, forestry, data center development, residential real estate, or commercial real estate.
- Deep expertise in a technique or data type outside our current areas of focus (e.g., imagery, lidar, forecasting, simulation) paired with a view on how it could help our product strategy.
- Comfort and an interest in supporting customer-facing demos, presentations, and discussions.
Aarden is a high-trust, high-output team. We’re striving to be intentional about our team growth. This allows us to test the outer boundaries of our individual capabilities, while also going deeper on developer tooling and support. You’ll work hard here, and we’ve got your back.
Practically, this means you’ll be asked to take on large projects, have a high bar of expectations to meet, and have a strong support system to help you meet that high bar. That support system includes:
- At least 2 in-person days per week | We’ve found that while heads-down time at home is fantastic for task-related productivity, in-person time is magic for longer-form productivity and relationship building. You’ll be expected to work at least 2 in-person days at either our headquarters in Seattle or our regional hub in Boulder, CO. In the latter case, you’ll also be expected to travel to Seattle on a periodic basis to meet with our full team. Our in-person days are used to plan, troubleshoot, and check-in with each other on progress and questions. Expect team lunches and whiteboarding.
- Focused ownership in your role | The rest of the team is here to help you and cares deeply about the long-term functionality of our applications. With that said, we’ll be looking to you to own your lane, go deep, and develop a strong stance on what it takes to make our applications best-in-class.
- Dedicated monthly AI tooling budget | We’re in a golden era of AI-powered developer tooling. We strongly encourage augmenting your output with AI tools, and have a dedicated & flexible budget for every team member to support that setup. We care about what you ship, not how.
The base pay range for this role is $150,000 – $190,000 per year.
Skills Required
- Strong data science and machine learning fundamentals
- Hands-on experience with geospatial data and tooling such as GDAL/OGR, Postgres/PostGIS, Apache Sedona, or similar
- Production-quality Python
- Fluent SQL
- Experience designing evaluation frameworks for geospatial, environmental, or socioeconomic models
- Experience working with scarce, imbalanced, or contested ground truth
- Experience using large language models in data pipelines or modeling
- Experience building documented, reproducible, and versioned data curation and model development workflows
- Ability to explain analytical outputs to non-experts and exercise good customer-focused judgment
- Experience in renewable energy, land conservation, forestry, data center development, residential real estate, or commercial real estate
- Expertise in imagery, lidar, forecasting, simulation, or another relevant technique or data type
- Comfort supporting customer-facing demos, presentations, and discussions
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The Company
What We Do
Aarden builds an interactive data platform for land-dependent investment decisions. It combines rich geospatial land and parcel data, infrastructure and nature data, market trends, community interests, and machine learning to help users explore land value, assess potential uses for individual parcels, and evaluate investment opportunities. The company serves landowners, institutional land investors, and capital allocators, helping them make faster, better-informed decisions about land and development.
