Skip to content

Experiment Faster and with Less Effort

Business Policy Experiments Using Fractional Factorial Designs

At DoorDash, we constantly strive to improve our experimentation processes by addressing four key dimensions, including velocity to increase how many experiments we can conduct,  toil to minimize our launch and analysis efforts, rigor to ensure a sound experimental design and robustly efficient analyses, and efficiency to reduce costs associated with our experimentation efforts.

Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings

Understanding the contents of a large digital catalog is a significant challenge for online businesses, but this challenge can be addressed using self-supervised neural network models.

The Undervalued Skills Candidates Need to Succeed in Data Science Interviews

After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances .

Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity

Data-driven companies measure real customer reactions to determine the efficacy of new product features, but the inability to run these experiments simultaneously and on mutually exclusive groups significantly slows down development.