<div><span style="font-size:16pt;">Welcome to Perennial.</span></div><div><br></div><div><span style="font-size:11pt;">Perennial is building the world’s leading verification platform for soil-based carbon removal. Our vision is to unlock soil as one of the world’s largest carbon sinks. To do that, we are building trusted standards, tools, and technologies to help verify climate-smart agriculture.</span></div><div><br></div><div><span style="font-size:11pt;">Perennial uses advanced remote measurement technology for soil carbon sequestration and emissions. We fuse machine learning, ground observations, and satellite data to map soil carbon and land-based GHG emissions at continent-level scales. This technology is powering the future of climate-smart agriculture and helping the food supply chain decarbonize.</span></div><div><br></div><div><span style="font-size:11pt;">At Perennial, you will work in a mission-driven and collaborative environment alongside a diverse team with backgrounds spanning science, technology, carbon markets, and agriculture.</span></div><div><br></div><div><span style="font-size:11pt;">Our headquarters is located in Boulder, CO USA. We are a fully-flexible company for remote and hybrid work.</span></div><div><br></div><div><span style="font-size:11pt;">We’re venture-backed by mission-aligned investors including Temasek, Bloomberg, Microsoft Climate Innovation Fund, SineWave Ventures, Alumni Ventures Group, and Collaborative Fund.</span></div><br><br><h3>Position Overview</h3><ul><li>As a Data Scientist, you will be responsible for algorithm and methodology development for an innovative company using machine learning and remote sensing data to quantify the benefits of regenerative agriculture at scale. The Perennial team is advancing the field of digital soil mapping (DSM) through impactful applied research, peer-reviewed science, and methodology development to bring DSM into the voluntary carbon markets (see <a href="https://verra.org/methodologies/tool-for-quantifying-soil-organic-carbon-stocks-using-data-driven-modeling-informed-by-remote-sensing/">our work with Verra</a>). You will help us quantify changes in soil organic carbon stocks in agricultural soils, deliver reliable results for our customers, and partner with our applied scientists and engineers to continually improve our models and processes that support a variety of carbon offset and Scope 3 projects.</li></ul><h3>What You'll Do</h3><ul><li>Build, improve, and deploy machine learning models for predicting soil carbon stock with remotely-sensed covariate data and limited training data</li><li>End-to-end deliveries for our customers: train models, run predictions, and ensure quality results are delivered in customer reports</li><li>Work with other data scientists, engineers, and policy experts to ensure that our data and methods comply with various standards and methodology requirements specific to a given project</li><li>Characterize the accuracy and uncertainty of model predictions and demonstrate the dependence of performance metrics on the surrounding context and parameters of carbon projects</li><li>Execute efficiently throughout full development cycle, from performing exploratory data analysis and initial R&amp;D to rapid prototyping and hardening models for production</li><li>Communicate your research internally and externally through detailed documentation, conference presentations, and peer-reviewed publications</li></ul><h3>What You'll Bring</h3><ul><li>Master's degree or Ph.D. in statistics, math, computer science, remote sensing, AI/ML, ecosystem science, geography, or a related STEM field</li><li>3–6 years of industry or research experience in data science, applied ML, geospatial analysis, or related fields</li><li>Strong proficiency in Python for data science (e.g. pandas, scikit-learn, xarray, numpy)</li><li>Experience building machine learning, statistical, or time series models informed by remotely-sensed data or large spatial datasets</li><li>Good communication and collaboration skills with functional and cross-functional teams</li><li>Ability to independently manage a project and deliver results</li></ul><h3>What Will Make You Stand Out</h3><ul><li>Experience working in the soil carbon MRV space and familiarity with relevant methodologies and tools (e.g. VM0042, VM0032, VMD0053)</li><li>Expertise in the open source geospatial python stack. Basic raster and vector operations, e.g. resampling, tiling, clipping, extracting, spatial statistics, harmonizing data </li><li>Experience quantifying uncertainty of spatial maps, or more generally geostatistics or spatial stats, esp. with machine learning</li><li>Experience working with Google Earth Engine and GCS</li><li>Startup experience or a strong entrepreneurial mindset (generally private company experience)</li></ul><h3>Why You'll Love Working Here</h3><ul><li>Mission-Driven Impact: Contribute directly to solving one of the most urgent global climate change challenges - soil health - through technological innovation.</li><li>High-Performance Culture: Join a talented, fast-paced team where your ideas and work drive meaningful impact.</li><li>Leadership &amp; Growth: Take ownership of projects, mentor others, and grow your leadership skills in a dynamic environment.</li><li>Work-Life Balance: Enjoy flexible work arrangements, generous PTO, and comprehensive health, dental, vision, HSA, and 401(k) benefits.</li><li>Strive for Excellence and Challenge the status quo: See some of our recent press about the problems we’re committed to solving. </li><li>Competitive compensation packages: Our team is our most valuable asset. We want everyone who works for us to feel fairly compensated for the impact they bring to our mission. The team member in this role can expect a starting salary in the range of $120k-145k alongside equity in the company. Compensation outside this range may be considered for exceptionally well-qualified candidates, depending on experience and expertise.</li><li>Perennial is an equal opportunity employer: We celebrate and embrace diversity and are committed to building a team that represents a variety of experiences, backgrounds, and skills. We do not discriminate on the basis of race, color, religion, marital status, age, gender identity, gender expression, sexual orientation, non-disqualifying physical or mental disability, national origin, veteran status, or other applicable legally protected characteristics.</li></ul><br>

<h2>Welcome to Perennial.</h2>
<p><span style="font-weight: 400;">Perennial is building the world&rsquo;s leading verification platform for soil-based carbon removal. Our vision is to unlock soil as one of the world&rsquo;s largest carbon sinks. To do that, we are building trusted standards, tools, and technologies to help verify climate-smart agriculture.</span></p>
<p><span style="font-weight: 400;">Perennial uses advanced remote measurement technology for soil carbon sequestration and emissions. We fuse machine learning, ground observations, and satellite data to map soil carbon and land-based GHG emissions at continent-level scales. This technology is powering the future of climate-smart agriculture and helping the food supply chain decarbonize.</span></p>
<p><span style="font-weight: 400;">At Perennial, you will work in a mission-driven and collaborative environment alongside a diverse team with backgrounds spanning science, technology, carbon markets, and agriculture.</span></p>
<p><span style="font-weight: 400;">Our headquarters is located in Boulder, CO USA. We are a fully-flexible company for remote and hybrid work.</span></p>
<p><span style="font-weight: 400;">We&rsquo;ve raised over $25M to date from mission-aligned investors including Temasek, Bloomberg, Microsoft Climate Innovation Fund, SineWave Ventures, Alumni Ventures Group, and Collaborative Fund.</span></p>

Data Scientist

<div><span>Welcome to Perennial.</span></div><div><br></div><div><span>Perennial is building the world’s leading verification platform for soil-based carbon removal. Our vision is to unlock soil as one of the world’s largest carbon sinks. To do that, we are building trusted standards, tools, and technologies to help verify climate-smart agriculture.</span></div><div><br></div><div><span>Perennial uses advanced remote measurement technology for soil carbon sequestration and emissions. We fuse machine learning, ground observations, and satellite data to map soil carbon and land-based GHG emissions at continent-level scales. This technology is powering the future of climate-smart agriculture and helping the food supply chain decarbonize.</span></div><div><br></div><div><span>At Perennial, you will work in a mission-driven and collaborative environment alongside a diverse team with backgrounds spanning science, technology, carbon markets, and agriculture.</span></div><div><br></div><div><span>Our headquarters is located in Boulder, CO USA. We are a fully-flexible company for remote and hybrid work.</span></div><div><br></div><div><span>We’re venture-backed by mission-aligned investors including Temasek, Bloomberg, Microsoft Climate Innovation Fund, SineWave Ventures, Alumni Ventures Group, and Collaborative Fund.</span></div><br><br><h3>Position Overview</h3><ul><li>As a Data Scientist, you will be responsible for algorithm and methodology development for an innovative company using machine learning and remote sensing data to quantify the benefits of regenerative agriculture at scale. The Perennial team is advancing the field of digital soil mapping (DSM) through impactful applied research, peer-reviewed science, and methodology development to bring DSM into the voluntary carbon markets (see <a href="https://verra.org/methodologies/tool-for-quantifying-soil-organic-carbon-stocks-using-data-driven-modeling-informed-by-remote-sensing/">our work with Verra</a>). You will help us quantify changes in soil organic carbon stocks in agricultural soils, deliver reliable results for our customers, and partner with our applied scientists and engineers to continually improve our models and processes that support a variety of carbon offset and Scope 3 projects.</li></ul><h3>What You'll Do</h3><ul><li>Build, improve, and deploy machine learning models for predicting soil carbon stock with remotely-sensed covariate data and limited training data</li><li>End-to-end deliveries for our customers: train models, run predictions, and ensure quality results are delivered in customer reports</li><li>Work with other data scientists, engineers, and policy experts to ensure that our data and methods comply with various standards and methodology requirements specific to a given project</li><li>Characterize the accuracy and uncertainty of model predictions and demonstrate the dependence of performance metrics on the surrounding context and parameters of carbon projects</li><li>Execute efficiently throughout full development cycle, from performing exploratory data analysis and initial R&amp;D to rapid prototyping and hardening models for production</li><li>Communicate your research internally and externally through detailed documentation, conference presentations, and peer-reviewed publications</li></ul><h3>What You'll Bring</h3><ul><li>Master's degree or Ph.D. in statistics, math, computer science, remote sensing, AI/ML, ecosystem science, geography, or a related STEM field</li><li>3–6 years of industry or research experience in data science, applied ML, geospatial analysis, or related fields</li><li>Strong proficiency in Python for data science (e.g. pandas, scikit-learn, xarray, numpy)</li><li>Experience building machine learning, statistical, or time series models informed by remotely-sensed data or large spatial datasets</li><li>Good communication and collaboration skills with functional and cross-functional teams</li><li>Ability to independently manage a project and deliver results</li></ul><h3>What Will Make You Stand Out</h3><ul><li>Experience working in the soil carbon MRV space and familiarity with relevant methodologies and tools (e.g. VM0042, VM0032, VMD0053)</li><li>Expertise in the open source geospatial python stack. Basic raster and vector operations, e.g. resampling, tiling, clipping, extracting, spatial statistics, harmonizing data&nbsp;</li><li>Experience quantifying uncertainty of spatial maps, or more generally geostatistics or spatial stats, esp. with machine learning</li><li>Experience working with Google Earth Engine and GCS</li><li>Startup experience or a strong entrepreneurial mindset (generally private company experience)</li></ul><h3>Why You'll Love Working Here</h3><ul><li>Mission-Driven Impact: Contribute directly to solving one of the most urgent global climate change challenges - soil health - through technological innovation.</li><li>High-Performance Culture: Join a talented, fast-paced team where your ideas and work drive meaningful impact.</li><li>Leadership &amp; Growth: Take ownership of projects, mentor others, and grow your leadership skills in a dynamic environment.</li><li>Work-Life Balance: Enjoy flexible work arrangements, generous PTO, and comprehensive health, dental, vision, HSA, and 401(k) benefits.</li><li>Strive for Excellence and Challenge the status quo: See some of our recent press about the problems we’re committed to solving.&nbsp;</li><li>Competitive compensation packages: Our team is our most valuable asset. We want everyone who works for us to feel fairly compensated for the impact they bring to our mission. The team member in this role can expect a starting salary in the range of $120k-145k alongside equity in the company. Compensation outside this range may be considered for exceptionally well-qualified candidates, depending on experience and expertise.</li><li>Perennial is an equal opportunity employer: We celebrate and embrace diversity and are committed to building a team that represents a variety of experiences, backgrounds, and skills. We do not discriminate on the basis of race, color, religion, marital status, age, gender identity, gender expression, sexual orientation, non-disqualifying physical or mental disability, national origin, veteran status, or other applicable legally protected characteristics.</li></ul><br>

Data Scientist

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About the Company

Perennial

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