Data Scientist

San Francisco

At Blend, we’re dedicated to improving lending. We’re an enterprise technology company, but our product affects the most important purchase most people will make in their lifetime—their home. For homebuyers, our product means a clear, guided path to a new home. For lenders, it means modern, easy-to-use tools that let employees spend their time helping customers, rather than on repetitive, manual tasks. By aligning and modernizing this archaic industry, we believe everybody wins.

We need someone who’s driven to solve hard problems—the harder the better. We’re motivated by the fact that our product won’t just affect the lives of a few people in the Bay Area—it affects people all over America, not to mention a foundational part of the U.S. economy. Founded in 2012 by former Palantir leaders, we’re currently backed by Founders Fund, Andreessen Horowitz and other prominent investors.

The Data Scientist will support data needs across our growing company. As an early member of our analytics team, you will help to define how we work with data and influence decision making throughout Blend with your insightful analysis and reporting. You will work closely with our Data Engineering teams, gaining expertise in data analysis, visualization, and infrastructure.

Our ideal Data Scientist has experience working in SQL, is familiar with Python or R, and understands statistics. You may be a great fit if you work well both independently and collaboratively, take ownership of your work, and can explain complicated ideas to just about anyone.


    • Prepare data for exploratory analysis, intelligent data products, and dashboards.
    • Collaborate with Data Engineers to develop and implement an ETL system for Blend’s rapidly growing data store.
    • Conduct exploratory analysis on our extensive datasets to discover valuable insights for our organization.
    • Maintain and improve our internal dashboard product.


    • Experience working in SQL, including joins, windowing functions and time series processing.
    • Familiarity with Python.
    • Understanding of fundamental probability and statistical concepts, such as hypothesis testing, maximum likelihood, and basic regression.
    • Interest or experience in machine learning techniques (such as regression, decision tree, and segmentation) is helpful.
    • Demonstrated ability to structure complex problems, derive insights from data, and communicate with diverse teams.
    • BS in a quantitative discipline.
    • Advanced degree is a plus.