Senior Data Scientist

Welcome to Perennial.

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.

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.

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.

Our headquarters is located in Boulder, CO USA. We are a fully-flexible company for remote and hybrid work.

We're venture-backed by mission-aligned investors including Temasek, Bloomberg, Microsoft Climate Innovation Fund, SineWave Ventures, Alumni Ventures Group, and Collaborative Fund.

About the Role:

As a Senior 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 our work with Verra). You will help us quantify changes in soil organic carbon stocks in agricultural soils, partner with our applied scientists and engineers to construct more informative features based on the latest scientific results, and further our knowledge of how various parameters and constraints impact model accuracy and uncertainty. In addition, you will help us mature our development processes as we develop prototype features and capabilities and turn them into robust products.

What You'll Own:

• Build, improve, and deploy machine learning models for predicting soil carbon stock with remotely-sensed covariate data and limited training data
• Develop new and more informative features for soil carbon prediction models to accurately and precisely determine changes in carbon stock over time
• Characterize the accuracy and uncertainty of model predictions and demonstrate the dependence of performance metrics on the surrounding context and parameters of carbon projects
• Execute efficiently throughout full development cycle, from performing exploratory data analysis and initial R&D to rapid prototyping and hardening models for production
• Work cross-functionally with Engineering, Product, and Commercial teams, serving as a subject matter expert in data science to help assess feasibility, definite requirements and scope, and influence initiatives on our Science Roadmap
• Ensure Perennial stays at the forefront of digital soil mapping by staying up to date on relevant literature and applying new ideas to drive innovation in our Science Team
• Communicate your research internally and externally through detailed documentation, conference presentations, and peer-reviewed publications

What You'll Bring:

• Ph.D. or Master's degree in statistics, math, computer science, remote sensing, AI/ML, ecosystem science, geography, or a related STEM field
• Strong coding skills in python
• Experience building machine learning, statistical, or time series models informed by remotely-sensed data
• Experience robustly quantifying prediction uncertainties with machine learning models
• Experience building and transforming prototype models into robust, production-ready capabilities
• Excellent communication and collaboration skills with functional and cross-functional teams
• Ability to independently manage a project and deliver results

What will make you stand out:

• Demonstrated experience developing and implementing robust development practices within a data science team
• Expertise in geospatial analysis/remote sensing tools and software. Basic raster and vector operations, e.g. resampling, tiling, clipping, extracting, spatial statistics, harmonizing data
• Experience quantifying uncertainty of spatial maps, or more generally geostatistics or spatial stats, esp. with machine learning
• Experience working with Google Earth Engine
• Startup experience or a strong entrepreneurial mindset (generally private company experience)

You'll love working at Perennial because:

• We live by our Core Values.
  • Speak your truths, welcome new voices.
  • Celebrate your successes, own your mistakes.
  • Solve important problems.
  • Invest in each other.
  • Build for the future.
  • Get your hands dirty!

• We challenge the status quo. We're a group of people who want to create the changes we hope to see in the world. See some of our recent press about the problems we're committed to solving.

• We invest in your life. We want to provide you with resources to meet your needs both in and outside of work. We offer generous PTO, health, vision, dental, 401k, and HSA benefits and a fully stocked kitchen to keep your mind sharp throughout the day.

• We want you to grow. We are a team that supports each others' professional and intellectual growth. You'll have access to regular opportunities to learn from teammates and invest in your professional development.

• We offer 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 USD $130,000 - $180,000 (commensurate with experience and location) alongside equity in the company.

• 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.