Job Description

The Decision Science organization at Square helps our sellers and our business grow by empowering decision making with data. The team provides Square with a variety of critical information such as marketing attribution, investment ROI, seller value, product behavior, and funnel performance. The team achieves this by building remarkable data infrastructure, analytics, data science, and machine learning solutions. The Measurement Machine Learning team within Decision Science plays an essential role in building best-in-class ML models and algorithms to inform strategic investment decisions with measurement.

In this role you’ll build machine learning models to forecast merchants’ revenues especially at an early stage. You will experiment with state-of-the-art algorithms to improve the prediction accuracies and use any and every dataset at your disposal (including 3rd party data) to engineer new features for the model. 

You will:

• Research and implement cutting-edge algorithms on revenue forecastings, especially improving early stage forecasting performance. 
• Familiar with Time Series model, especially deep learning for Time Series. 

Stack: 

We value general engineering skills and versatility over existing domain knowledge, but here’s a sample of what we currently use:

• Python, Java 
• Tensorflow and PyTorch 
• AWS, GCP
• SQL, Tableau & Mode

Qualifications

You have: 

• Must be currently enrolled in a PhD program in Statistics, Computer Science, Economics, or a related field.
• Available to work full-time remotely for 3 months in Summer 2023
• A strong track record of performing machine learning model development using Python (numpy, tensorflow, pytorch, scikit-learn, etc.). 
• Knowledge of modern techniques in machine learning and deep learning, e.g., time-series, neural network architectures, with an orientation to maximizing such algorithms in a large scale production setting
• The ability to clearly communicate complex results to technical and non-technical audiences
• Interns are not required to be physically located in their mentor’s worksite location for the duration of their internship