How can we sustainably feed the world? With the population set to reach nine billion by 2050 and available farmland decreasing, the odds are stacked against us. This problem falls squarely on the shoulders of farmers, who are increasingly working harder for less. FBN helps farmers to solve these problems.
Farmers are often forced to make key decisions in the face of significant uncertainty about their impact. FBN applies technology and data science to this challenge. We’re building the world’s largest agronomic dataset – made up of billions of data points, gathered from sophisticated sensors on farm equipment. The network effect of farmers sharing their data allows us to deliver unprecedented analyses that help farmers make data-driven decisions – we’re not just building new technology, we’re also making new agricultural scientific discoveries.
Data science underlies every product that we build at FBN, so you’ll see your work directly translate into new products for our farmers. The Data Science Team at FBN:
- Explores one of the world’s most unique, interesting, and diverse datasets
- Furthers scientific understanding about what affects agricultural production
- Uncovers insights that help farmers efficiently support a growing population
- Makes meaningful improvements in the world by translating data science into products that help farmers increase yield and efficiency
We’re looking for a Data Scientist, Credit, Fraud and Risk to help us better understand our farmer customers. More specifically, this role will:
- Build, maintain and improve FBN’s proprietary credit model(s) for our input, operating, equipment and land lending
- Interface with capital market partners’ credit committees, where needed
- Develop algorithms to detect potential fraud across our platform (e.g., loan applications, crop insurance applications, crop insurance claims, etc.)
- Identify anomalous user behavior that might be associated with bad actors (e.g., store fraud, fake logins, fake user accounts, etc.)
- Produce data products that support securitization
- Build scalable data pipelines, models and frameworks that help our teams better serve our farmer customers
- Design, implement and analyze hypothesis-driven experiments to optimize the performance of our credit and fraud detection efforts
- 5+ years working with lending data (e.g., repayment history, loan tapes, credit modeling and risk assessment, etc.) to drive key business outcomes
- Experience building and or leveraging unique and proprietary data systems that combine data from many sources (e.g., public, private, user, derived insights) to drive business outcomes.
- Deep understanding of statistical modeling concepts
- Familiarity with machine learning algorithms
- Excellent programming skills (R or Python preferred)
- Familiarity with cloud computing and version control
- MS or greater in statistics or related discipline
The following represents FBN’s reasonable estimate of the US national average base salary range of possible compensation for this role based on market data and placement of internal employees: $120,000 – $135,000.
This salary range may vary based on geography and variations in cost of labor. Beyond the above or adjusted to market salary range, FBN also offers all full-time employees competitive equity compensation, health and wellness benefits, and various perks.
To apply for this job please visit boards.greenhouse.io.