Data Scientist

Funga PBC•Austin, TX

7d•$90,000 - $130,000•Remote

About The Position

Funga is a public benefit corporation harnessing forest fungal networks to address the climate crisis. We combine modern DNA sequencing and machine learning technology with breakthrough research on the forest microbiome to put the right native, biodiverse communities of mycorrhizal fungi in the right place. This leads to more quality wood created more quickly, more carbon sequestered, and more resilient forests. We serve land partners seeking to implement regenerative forestry practices that improve productivity and sustainability, and corporate carbon buyers building high-quality carbon removal portfolios with strong social and biodiversity outcomes. We’re a team of passionate scientists and builders on a mission to draw down at least three gigatons of carbon dioxide from the atmosphere by 2050. If you are excited by starting new things, learning as you go, and being hands-on in applying your work to the private sector, you will dig working with us. We encourage you to apply even if only some of the below criteria are met - just make sure to tell us why you’re excited about Funga! We are looking for a Data Scientist/Statistician to support our work analyzing forest productivity, and experimental data. You will collaborate with the team to scale our ability to process and interpret ecological and experimental data, contributing directly to Funga’s mission. We need a pragmatic builder who thrives on turning complex raw data into actionable models. This is a role for someone who balances deep scientific rigor with the urgency and velocity required to accelerate our impact.

Requirements

An MS or Ph.D. in a quantitative field (Statistics, Applied Mathematics, or equivalent experience).
Demonstrated expertise in hierarchical/mixed-effects models, longitudinal data analysis, repeated measures methods, and spatio-temporal modeling. You know how to handle the autocorrelation and nesting inherent in experimental ecological data.
High proficiency in SQL, R, and Python for data manipulation and modeling.
Practical experience applying machine learning techniques to biological, environmental, or similarly complex systems.
Familiarity with data integration, version control (Git), and reproducible research workflows (e.g. Markdown, Jupyter, Quarto).
Pragmatism balanced with rigor. You prioritize rigorous, actionable insights over academic perfection. You know when a simple linear model is better than a deep learning model.
A passion for biodiversity, ecosystem health, and climate action.

Nice To Haves

Background in forest ecology, soil microbial ecology, or experimental design for field trials is a major plus.
Experience with geospatial and remote sensing data analysis is a plus.

Responsibilities

Perform robust statistical analyses of experimental and observational forest data.
Evaluate and enhance current experimental design by performing power analyses and applying statistical models to multi-year trials.
Rapidly prototype and deploy models connecting forest productivity to soil microbiomes and environmental factors, accounting for spatial and temporal dependencies.
Enhance our carbon accounting system to incorporate incoming field-collected, genomic, and remotely sensed data.
Build and validate pipelines for assessing the effectiveness of satellite and drone datasets for capturing on-the-ground measurements.
Work closely with internal stakeholders on Growth, Applied Science, and other teams to ensure that insights meet their needs and accelerate their work.
Travel up to 10-15% of the time for team offsite events, data collection, and stakeholder meetings as needed.