4. Akshay Singh - The thin line between data science and data engineering
Towards Data Science
09/10/19
•49m
About
Comments
Featured In
If you’ve been following developments in data science over the last few years, you’ll know that the field has evolved a lot since its Wild West phase in the early/mid 2010s. Back then, a couple of Jupyter notebooks with half-baked modeling projects could land you a job at a respectable company, but things have since changed in a big way.
Today, as companies have finally come to understand the value that data science can bring, more and more emphasis is being placed on the implementation of data science in production systems. And as these implementations have required models that can perform on larger and larger datasets in real-time, an awful lot of data science problems have become engineering problems.
That’s why we sat down with Akshay Singh, who among other things has worked in and managed data science teams at Amazon, League and the Chan-Zuckerberg Initiative (formerly Meta.com).
Previous Episode
Susan Holcomb - Nontechnical career skills for data scientists
August 14, 2019
•50m
It’s easy to think of data science as a technical discipline, but in practice, things don’t really work out that way. If you’re going to be a successful data scientist, people will need to believe that you can add value in order to hire you, people will need to believe in your pet project in order to endorse it within your company, and people will need to make decisions based on the insights you pull out of your data.
Although it’s easy to forget about the human element, managing it is one of the most useful skills you can develop if you want to climb the data science ladder, and land that first job, or that promotion you’re after. And that’s exactly why we sat down with Susan Holcomb, the former Head of Data at Pebble, the world’s first smartwatch company.
When Pebble first hired her, Susan was fresh out of grad school in physics, and had never led a team, or interacted with startup executives. As the company grew, she had to figure out how to get Pebble’s leadership to support her effort to push the company in a more data-driven direction, at the same time as she managed a team of data scientists for the first time.
Next Episode
5. Rocio Ng - Data science and product management at LinkedIn
September 19, 2019
•45m
Most software development roles are pretty straightforward: someone tells you what to build (usually a product manager), and you build it. What’s interesting about data science is that although it’s a software role, it doesn’t quite follow this rule.
That’s because data scientists are often the only people who can understand the practical business consequences of their work. There’s only one person on the team who can answer questions like, “What does the variance in our cluster analysis tell us about user preferences?” and “ What are the business consequences of our model’s ROC score?”, and that person is the data scientist. In that sense, data scientists have a very important responsibility not to leave any insights on the table, and to bring business instincts to bare even when they’re dealing with deeply technical problems.
For today’s episode, we spoke with Rocio Ng, a data scientist at LinkedIn, about the need for strong partnerships between data scientists and product managers, and the day-to-day dynamic between those roles at LinkedIn. Along the way, we also talked about one of the most common mistakes that early career data scientists make: focusing too much on that first role.
If you like this episode you’ll love
Promoted