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Machine Learning Wildfire Scientist

Who we are: First Street is the industry standard for physical climate risk data. We use transparent and peer-reviewed methodologies to calculate the past, present, and future climate risk for every property in the world. We started eight years ago by working with the world’s leading climate scientists to create groundbreaking, climate-adjusted, property specific models and haven’t stopped. 

Our mission: We exist to connect climate change to financial risk

Our data: We create physics-based, deterministic models of flooding, wildfire and hurricanes, and advanced statistical models of extreme heat, air quality, drought, hail, severe convective storms, winter storms, and more. All of this data is used to create property-level financial risk metrics and macroeconomic variables to quantify the impacts of climate change. 

Our customers: We empower governments at the highest levels to make smart regulations, businesses to avoid bad investments, and everyday Americans to understand their personal risk from climate change. We are relied on every day by:

  • Agencies ranging from the U.S. Department of Treasury to Fannie Mae

  • The world's biggest banks such as Bank of America and Wells Fargo 

  • Institutional investors like Nuveen and Blackstone 

  • Millions of users on Zillow, Redfin, Realtor.com, Homes.com, and more 

We believe:  Our work needs to match the pace and scope of the climate problem. This is why we have invested tens of millions of dollars into our science, data, people, and products and have raised tens of millions more to move even faster.  Read more about our culture here and see what Climate Risk Financial Modeling is all about here.

Come join us and use your talents to create solutions to address humanity's biggest problem.

Team & Role Overview: 

We are looking for a Scientist with expertise in data analysis and modeling of wildfires and the fire environment, strong machine learning and data science skills, and demonstrable experience working with remotely sensed data sources to join our team. This person will need to have a Ph.D. in wildfire science or a related field and ideally will have postdoctoral professional experience. In addition to technical skills and subject matter expertise, they will also have the ability to lead projects and collaborate closely with other scientists. 

What you’ll do: 

  • Collaborate with our wildfire, climate science, and data science teams to build global wildfire risk models.

  • Estimate wildfire fuels using a combination of in situ and remotely sensed data.

  • Introduce innovative methods to improve the wildfire risk model performance and assess fire risk at a 30m spatial resolution.

  • Expertise in the formulation and use of FM40 fuel classification for Rothermel-based wildfire behavior models.

  • Work with the team to manage fire model production, quality control and ongoing evaluation/validation efforts.

  • Identify and develop new data inputs and methodological improvements for inclusion into the First Street Wildfire Model.

What you’ll need: 

  • Ph.D. in wildfire science or a related subject, with experience in fire behavior modeling and quantitative analysis of the fire environment.

  • Strong foundation in understanding of modeling fire behavior and wildfire processes; experience modeling fuels layers and vegetation would be particularly desirable.

  • Strong understanding of statistics and Machine Learning and how they can be applied to vegetation, fuels, wildfire and climate models.

  • Expertise with remotely sensed data, big data analysis, large-capacity processing workflows, and cloud computing.

  • Expertise in various fire behavior modeling methods, especially Rothermel-based models (e.g., ELMFIRE or FSIM) or similar modeling frameworks.

  • Experience with wildfire behavior and risk modeling in the Wildland Urban Interface (WUI).

  • Expertise in probability and statistics related to fire ignition analysis.

  • Expertise using both compiled and scripted languages (e.g., Matlab, Python, UNIX shell, C++, Fortran, and/or SQL) and GIS software (e.g., QGIS and ArcGIS) to efficiently analyze outputs.

  • Ability to work in a fast-paced team environment.

What will make you stand out:

  • Experience authoring peer-reviewed scientific publications in areas related to wildfires and fire risk.

  • Skill/Experience developing a variety of satellite-based wildfire fuel maps for use in Rothermel-based fire behavior models.

  • Skill/Experience in creating/evaluating/validating fire behavior models and their inputs, and quantitatively evaluating model performance.

  • Prior experience in a fast-paced startup environment

How we work: 

  • Impact: We only focus on things that move the needle 

  • Drive: We are driven by the role we play in connecting climate change to financial risk 

  • Ownership: This is our company and we act accordingly

  • Urgency: We move quickly because the world depends on it 

  • Resilience: We have a growth mindset in all that we do

What we offer: 

  • Competitive salary commensurate with experience 

  • Ownership interest in the company via Employee Stock Option Plan 

  • Hybrid Schedule with in-office work days on Monday, Wednesday and Thursday 

  • 15 vacation days along with 13 company holidays and 10 sick days 

  • Health benefits covered at 100% for employee or a significant contribution for family plans 

  • Vision and dental benefits with partial employee contribution

  • 12 weeks of paid parental leave 

  • Access to One Medical, Teledoc, HealthAdvocate, Kindbody, and Talkspace

  • Company 401k program 

  • Commuter benefits 

  • Life Insurance

  • Tech startup environment 

  • Weekly team meals and an office stocked with coffee and snacks 

  • Working on the world’s biggest issue with other passionate professionals 

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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What You Should Know About Machine Learning Wildfire Scientist, First Street

Are you passionate about combating climate change through innovative science? Look no further! First Street is on the hunt for a dynamic Machine Learning Wildfire Scientist to join our incredible team. At First Street, we are dedicated to providing transparent and peer-reviewed methodologies that accurately assess climate risk for properties worldwide. This role offers a chance to collaborate with passionate climate scientists and skilled data analysts to develop cutting-edge global wildfire risk models. You’ll have the opportunity to dive deep into wildfire fuels using a blend of in situ and remotely sensed data. By introducing innovative methods to enhance our wildfire risk model, you’ll play a crucial role in predicting fire risks at a fine spatial resolution. Your expertise in modeling fire behavior and quantitative analysis will be essential, as we’re looking for someone with a Ph.D. in wildfire science or a related field, complemented by hands-on experience in fire behavior modeling. Join us in our mission where every day you’ll contribute to quantifying the impacts of climate change. Be part of a team that directly influences policies by providing critical data to governments, banks, and individuals. If you have a passion for using science and technology to create meaningful solutions for humanity’s biggest challenges, we encourage you to apply today and help shape a resilient future!

Frequently Asked Questions (FAQs) for Machine Learning Wildfire Scientist Role at First Street
What responsibilities does a Machine Learning Wildfire Scientist at First Street have?

As a Machine Learning Wildfire Scientist at First Street, you will be responsible for collaborating with cross-functional teams to build and enhance global wildfire risk models. This includes estimating wildfire fuels using various data sets, introducing innovative methods for model performance improvement, and managing fire modeling production and quality control. Your role will also involve identifying and developing new data inputs to enhance the First Street Wildfire Model, ensuring it remains at the forefront of climate risk assessment.

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What qualifications are required for the Machine Learning Wildfire Scientist position at First Street?

To qualify for the Machine Learning Wildfire Scientist role at First Street, candidates must hold a Ph.D. in wildfire science or a related subject. Demonstrable experience in fire behavior modeling, quantitative analysis, and strong machine learning capabilities are essential. Furthermore, expertise with remotely sensed data and large-scale data processing workflows is vital. Experience with various programming languages and GIS software is also highly desired.

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How does First Street use machine learning in wildfire modeling?

At First Street, machine learning plays a pivotal role in analyzing large datasets related to wildfire behavior and risk. Our Machine Learning Wildfire Scientist leverages advanced statistical methods to improve the performance of wildfire risk models, enabling us to assess fire risks with high spatial resolution. By integrating machine learning techniques into our work, we are better equipped to predict future wildfire scenarios and their financial impacts driven by climate change.

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What does the work environment look like for a Machine Learning Wildfire Scientist at First Street?

The work environment for a Machine Learning Wildfire Scientist at First Street is dynamic and collaborative. You will be part of a team of passionate professionals committed to addressing climate change. Our hybrid work model allows for flexibility, with in-office days designed to foster teamwork and innovation. You will engage in weekly team meals and benefit from a culture that encourages growth and resilience in a fast-paced tech startup atmosphere.

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What opportunities are available for career growth as a Machine Learning Wildfire Scientist at First Street?

At First Street, we are deeply committed to the professional development of our team members. As a Machine Learning Wildfire Scientist, you will have the opportunity to lead projects, collaborate with experienced scientists, and contribute to peer-reviewed publications in the field. Your contributions will directly impact our mission and enable you to grow your skills in climate risk modeling while working on groundbreaking research that addresses one of humanity's most pressing challenges.

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Common Interview Questions for Machine Learning Wildfire Scientist
Can you describe your experience with fire behavior modeling?

In answering this question, focus on specific projects where you applied fire behavior modeling techniques. Highlight any tools or methodologies you used, such as Rothermel-based models. Discuss how your work contributed to understanding wildfire dynamics and improving risk assessments.

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How have you utilized machine learning in environmental data analysis?

Provide specific examples of projects where you implemented machine learning techniques to analyze environmental data. Discuss the methods you used, the results achieved, and how this experience aligns with First Street's mission in wildfire risk assessment.

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What approaches do you take to collaborate with cross-disciplinary teams?

Share strategies that you use to foster collaboration, such as regular communication, setting common goals, and actively seeking input from team members in different specialties. It’s essential to demonstrate how this collaboration is vital in holistic climate risk modeling.

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Describe your experience with remotely sensed data.

Detail your experience working with remotely sensed data relevant to wildfire science. Explain the types of data used, how you processed it, and how it contributed to your models or analyses, emphasizing its importance in improving wildfire risk estimations.

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What challenges have you faced when developing wildfire risk models?

Discuss specific challenges you encountered, such as data limitations or integrating various modeling techniques. Focus on how you approached these challenges, the innovative solutions you devised, and the outcomes of your efforts.

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How do you stay current with developments in wildfire science and machine learning?

Mention your methods for staying informed, such as attending conferences, participating in workshops, and following relevant journals and publications. Highlight how continuous learning helps you bring the latest insights and techniques to your work at First Street.

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Can you give an example of a quantitative analysis project you've completed?

Provide a specific example related to wildfire science or risk assessment, explaining the data used, the methodology applied, and the insights gained from the analysis. Emphasize the impact this work had, whether it was in a research context or practical application.

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What programming languages and tools are you proficient in?

List the programming languages and tools relevant to the Machine Learning Wildfire Scientist role, such as Python, MATLAB, or GIS software. Discuss how you have used these tools in your previous work to achieve specific outcomes or streamline processes, demonstrating your technical capabilities.

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What role does statistical analysis play in your wildfire research?

Discuss the significance of statistical analysis in your research. Explain how you use statistics to analyze risk factors, model predictions, and validate outcomes. Highlight any specific statistical methods or software you are experienced with, and their relevance in wildfire modeling.

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Why do you want to work at First Street as a Machine Learning Wildfire Scientist?

Articulate your motivation for applying, emphasizing your passion for climate science and risk modeling. Discuss how First Street's mission aligns with your values and what unique contributions you believe you can make to the team in advancing their goals.

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DATE POSTED
March 18, 2025

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