As the Healthcare landscape becomes increasingly awash in digital information, data scientists are operating in areas that have more traditionally been the domain of life scientists. At Amgen, one of the world’s largest biotechnology companies, data scientist Kerry Weinberg has happily found herself in the thick of this change as she helps the company utilize the tools of data analytics to develop and deliver medicines to patients with serious illnesses. We caught up with Weinberg between dips into Amgen’s data lake (a non-watery place where a good of deal of Amgen’s data has been integrated) to get some insight into how Amgen is digging deep into data across the organization to use it more effectively.

How would you characterize the shift into data science for life sciences?

Traditional data science techniques have been used in the life sciences for a while in pocket areas. What we are seeing now is a shift to doing much higher computation in scenarios where you have on the order of thousands to millions of data points. In these situations, you rely more heavily on the algorithm to tell you what the answer is rather than your a priori understanding.

Give me some examples of how data analytic techniques are being applied at Amgen?

In manufacturing, we are collecting and analyzing data points every second across hundreds of sensors at production facilities globally. In R&D, we are using high-throughput systems to run more assays, an analytic procedure to efficiently evaluate new molecules or compounds, in parallel to focus our research. And from a commercial standpoint, we are using data and analytics to explore how healthcare providers and patients interact with our products, how they could be improved, what we might learn from an access standpoint, and what factors are contributing to appropriate adherence.

Where is Amgen in its data management journey?

A lot of the data models available today are data hungry and if you don’t have the data integration in place you are not going to take advantage of this sophistication. Former classmates of mine have gone to very large industrial companies. For them, it’s 80 percent getting data and figuring out what to do with it and 20 percent of the fun stuff. We don’t have that problem. What we really have going for us right now is that we have done a lot of investment in data integration.

What do you think life scientists need to learn from data scientists?

Typically, the missing piece for life scientists is that they don’t have the software experience. They don’t know how to code. You need to be able to build models. I helped start a three-month program at Amgen where we trained life scientists how to code. These are people who might have studied chemical engineering or biology. Now they can then build models that predict what will happen prior to implementation.

What draws data scientists to Amgen?

Data scientists want to know if the data sets they are going to be working on are interesting and if the problems they will be trying to solve are meaningful. At Amgen, the answer is yes to both. There are all kinds of data sets here. We are measuring genomic and proteomic [protein-based] data, activity in manufacturing plants, and across our global supply chain. From a commercial standpoint, we are measuring human dynamics. Just about any flavor of data you want, you can get it here. Plus, the larger problems that Amgen is trying to solve—which have to do with developing medicines for patients who have debilitating illnesses like cancer and cardiovascular disease—are far more motivating, more interesting, and more diverse than what you might find in other industries. I guess I could go work for a hedge fund and figure out how to optimize trades and make them more efficient but that’s not what gets me up in the morning.



Weinberg actually might be better off working at her current job than at a hedge fund. The global life sciences market is expected to grow at a 14.3 percent CAGR from 2018 to 2025, hitting a $39.8 billion valuation that year. It’s not hard to see why. The addition of new technologies to harness data, an aging population and the potential to find a cure or treatment for chronic disease would make anyone optimistic, not only for life sciences, but for the future as well.

Learn more at

This story was produced by the WIRED Brand Lab for Amgen and has been republished under Creative Commons