SilviaTerra
San Francisco
11-50 employees
Expanding our collective understanding of forests and strengthen our ability to manage these complex and vital ecosystems.
Prior Listings
Data Scientist - Statistical Modeling
Data Scientist - Statistical Modeling
This job is no longer open
About SilviaTerra
SilviaTerra helps mission-driven companies buy carbon credits from American forest owners. Based on a decade of work with America's largest landowners, our data-driven platform brings gigatons of new carbon offsets to market. Our mission is to work with every forest owner to unlock the full value of every acre.
Read more in this TechCrunch feature article about SilviaTerra.
We are backed by some of the world’s leading VC firms who were early investors in companies like Twitter, Etsy, Kickstarter, and Coinbase. Our global team of foresters, scientists, and technologists hails from leading research institutions and high growth technology startups.
We are looking for passionate, collaborative, and entrepreneurial individuals to join the team. If you like fast-paced environments and want to help us build the future of forestry, then we’d love to speak to you!
Job Description
SilviaTerra is built on terabytes of geospatial data, and we are rapidly expanding to integrate new types and structure of data into our work. Our flagship data product is Basemap, a high-resolution dataset of estimated number, species and size of trees across America. The Data Science team at SilviaTerra is responsible for developing, improving, and extending this dataset to new geographies. We are also focused on developing and improving metrics (biodiversity indices, harvest likelihood, etc.) based on these data. Basemap and its derivative metrics are critical components that underpin transactions on our carbon market.
This role is an integral part of that work, working closely with the rest of our team of forest biometricians and data scientists on model development and integrating new sources and types of measurements. We are continually working to improve the accuracy and efficiency of Basemap and its derivatives as we grow the Natural Capital Exchange.
Our ideal candidate is a statistical or machine learning professional who deeply understands a broad array of modeling approaches and techniques, who thinks hard about uncertainty, and who takes a pragmatic, applied approach to model development. If that's you, we want to meet you!
This is a remote position.
Key Responsibilities
- Work within a collaborative team to develop, evaluate and improve modeling and prediction processes, toolchains, and workflows
- Provide statistical guidance on sample design and data integration to support modeling and prediction
- Develop and apply methods for summarizing and reporting uncertainty
Required Skills & Experience
- Deep understanding of and applied experience using a variety of machine learning and statistical techniques
- Experience with cloud-based computing infrastructure such as AWS or Azure
- Familiarity with statistical sampling theory and sampling designs
- Fluent ability in Python or R (preferably both)
Desired Skills & Experience
- Experience using probabilistic programming languages such as Stan, PyMC3, Pyro, Turing, or Edward
- Familiarity with Bayesian hierarchical modeling, regularization, and nonparametric techniques such as splines and Gaussian processes
- Experience with time series and spatial modeling techniques
- Familiarity with forest inventory, management, and ecology data
- Familiarity with remote sensing sensors and data
- Experience with geospatial and remote sensing analyses using open-source software (QGIS, R and Python packages)
- Detail-oriented
- Excellent written communication skills
- Comfortable working remotely with necessary travel for company meetings.
SilviaTerra is an equal opportunity employer and will not discriminate against any employee or applicant on the basis of age, color, disability, gender, national origin, race, religion, sexual orientation, veteran status, or any classification protected by federal, state, or local law. Applicants must be authorized to work in the United States.
This job is no longer open