PODCAST
Trends in data science
Sadie St. Lawrence on founding Women in Data, the potential data advantages of the blockchain, and how data science is evolving
Editor’s note: The TDS Podcast is hosted by Jeremie Harris, who is the co-founder of Mercurius, an AI safety startup. Every week, Jeremie chats with researchers and business leaders at the forefront of the field to unpack the most pressing questions around data science, machine learning, and AI.
As you might know if you follow the podcast, we usually talk about the world of cutting-edge AI capabilities, and some of the emerging safety risks and other challenges that the future of AI might bring. But I thought that for today’s episode, it would be fun to change things up a bit and talk about the applied side of data science, and how the field has evolved over the last year or two.
And I found the perfect guest to do that with: her name is Sadie St. Lawrence, and among other things, she’s the founder of Women in Data — a community that helps women enter the field of data and advance throughout their careers — and she’s also the host of the Data Bytes podcast, a seasoned data scientist and a community builder extraordinaire. Sadie joined me to talk about her founder’s journey, what data science looks like today, and even the possibilities that blockchains introduce for data science on this episode of the towards data science podcast.
Here were some of my favourite take-homes from the conversation:
- Sadie’s journey as a founder started with an impromptu experiment: a simple meet-up for women interested in landing careers in data. And like most early startup experiences, it was almost a failure: 15 minutes into her inaugural Women in Data event, she was the only attendee. But a few minutes later, one participant arrived, with a handful more in tow. This small, core group would eventually nucleate the globe-spanning network that Women in Data has become today, preserving the intimate and supportive culture that developed in Sadie’s first conversation with her early adopters. Sadie points to that culture as Women in Data’s secret sauce, and the key factor in the growth of the organization. We talked quite a bit about founder effects and culture propagation in successful startups.
- The increasing adoption of blockchain technology is something Sadie sees as a major opportunity for data-oriented companies and data scientists. Because blockchains function as a structured, reliable single source of truth for financial data, they offer an unusually clear perspective on the flow of resources through an ecosystem. On a blockchain, transactions are logged and executed at the same time —in fact, the record of a transaction is its execution, which makes blockchain data unusually close to ground truth, and therefore all the more reliable. All of this (plus significant developments in graph-based approaches to ML) make blockchains a promising new source of data, which has yet to be fully exploited. Sadie argues that it’s worth learning the ins & outs of that tech if you’re a budding or experienced data scientist.
- We talked a fair bit about community building and how it’s changed (and hasn’t) since the early days of Women in Data. One of the key changes Sadie’s observed is the feasibility of remote networking — both because people are more comfortable networking over Zoom theses days, and because a new generation of tooling designed for remote community building has made new modes of communication possible. However, Sadie also points to a few key things that haven’t changed. Chief among them is the need for in-person events, because they allow for a greater level of intimacy than online venues. It’s easy to set up a Slack community with a bunch of semi-passive members, but the deepest sense of community still comes from interactions in the real world.
- Many jobseekers think of “data science” roles as their ultimate goal, but with experience, they often end up preferring other jobs. It’s easy to under-value less flashy roles in data analytics and data engineering, for example. Apart from being a great long-term fit for many applicants in and of themselves, these kinds of jobs are often really good entry points into a company’s data-focused teams (which can tee you up for a data science role if that’s what you’re looking for in the long run).
You can follow Sadie on Twitter here, or me here.
Chapters:
- 0:00 Intro
- 2:00 Founding Women in Data
- 6:30 Having gendered conversations
- 11:00 The cultural aspect
- 16:45 Opportunities in blockchain
- 22:00 The blockchain database
- 32:30 Data science education
- 37:00 GPT-3 and unstructured data
- 39:30 Data science as a career
- 42:50 Wrap-up
