Using R Plumber and Slack to Run Data Science Pipelines
Using R Plumber and Slack to Run Data Science Pipelines Last year I published a starter guide that explains how we used the R…
Four Principles of Data Science Ethics at Retina
Does your data science team worry about aligning your work with your values? Data science and machine learning provide new capabilities, and with those come…
Clustering Messy Time Series
When clustering data, it can be challenging to handle time series data, especially when the series have differing lengths. As part of my PhD work…
Customer Lifetime Value Migration Over Time
Customer lifetime value (CLV) is not a static metric. It changes over time—sometimes dramatically—as you observe new data about a customer. At Retina, we analyze…
Goal-Driven Customer Segmentation
Customer segmentation is a powerful technique for understanding your customers. However, it can be tricky to successfully implement it in a business context. In this…
5 Steps to Build an Effective Data Science Team
I’ve built and run data teams at Paypal, Facebook, and now at Retina AI. In each of those roles, I’ve also been asked how to…
Data Science User Experience: Lost In Translation
If you ever get the chance to talk to a VP of Analytics or a Head of Data Science, you will find that most have…
How to Make a Customer Lifetime Value Prediction
How much is each one of your customers worth? How much should you spend to acquire them? What about to retain them? Customer lifetime value…
The Lifetime Value of Your Professional Relationships
How important is your co-worker to you? How about your manager or mentor? Let’s use data science to explore the value of professional relationships. Start…