Towards Data Science

One thing that you might not realize if you haven’t worked as a data scientist in very large companies is that the problems that arise at enterprise scale (and well as the skills that are needed to solve them) are completely different from those you’re likely to run into at a startup.

Scale is a great thing for many reasons: it means access to more data sources, and usually more resources for compute and storage. But big companies can take advantage of these things only by fostering successful collaboration between and among large teams (which is really, really hard), and have to contend with unique data sanitation challenges that can’t be addressed without reinventing practically the entire data science life cycle.

So I’d say it’s a good thing we booked Sanjeev Sharma, Vice President of Data Modernization and Strategy at Delphix, for today’s episode. Sanjeev’s specialty is helping huge companies with significant technical debt modernize and upgrade their data pipelines, and he’s seen the ins and outs of data science at enterprise scale for longer than almost anyone.

A few years ago, there really wasn’t much of a difference between data science in theory and in practice: a jupyter notebook and a couple of imports were all you really needed to do meaningful data science work. Today, as the classroom overlaps less and less with the realities of industry, it’s becoming more and more important for data scientists to develop the ability to learn independently and go off the beaten path.

Few people have done so as effectively as Sanyam Bhutani, who among other things is an incoming ML engineer at H2O.ai, a top-1% Kaggler, popular blogger and host of the Chai Time Data Science Podcast. Sanyam has a unique perspective on the mismatch between what’s taught in the classroom and what’s required in industry: he started doing ML contract work while still in undergrad, and has interviewed some of the world’s top-ranked Kagglers to better understand where the rubber meets the data science road. 

The trend towards model deployment, engineering and just generally building “stuff that works” is just the latest step in the evolution of the now-maturing world of data science. It’s almost guaranteed not to be the last one though, and staying ahead of the data science curve means keeping an eye on what trends might be just around the corner. That’s why we asked Ben Lorica, O’Reilly Media’s Chief Data Scientist, to join us on the podcast.

Not only does Ben have a mile-high view of the data science world (he advises about a dozen startups and organizes multiple world-class conferences), but he also has a perspective that spans two decades of data science evolution.

Each week, I have dozens of conversations with people who are trying to break into data science. The main topic of the conversations varies, but it’s rare that I walk away without getting a question like, “Do you think I have a shot in data science given my unusual background in [finance/physics/stats/economics/etc]?”.

From now on, my answer to that question will be to point them to today’s guest, George John Jordan Thomas Aquinas Hayward.

George [names omitted] Hayward’s data science career is a testament to the power of branding and storytelling. After completing a JD/MBA at Stanford and reaching top-ranked status in Hackerrank’s SQL challenges, he went on to work on contract for a startup at Google, and subsequently for a number of other companies. Now, you might be tempted to ask how comedy and law could possibly lead to a data science career.

For today’s podcast, we spoke with someone who is laser-focused on considering this second possibility: the idea that data science is becoming an engineer’s game. Serkan Piantino served as the Director of Engineering for Facebook AI Research, and now runs machine learning infrastructure startup Spell. Their goal is to make dev tools for data scientists that make it as easy to train models on the cloud as it is to train them locally. That experience, combined with his time at Facebook, have given him a unique perspective on the engineering best practices that data scientists should use, and the future of the field as a whole.

I’ve said it before and I’ll say it again: “data science” is an ambiguous job title. People use the term to refer to data science, data engineering, machine learning engineering and analytics roles, and that’s bad enough. But worse still, being a “data scientist” means completely different things depending on the scale and stage of the company you’re working at. A data scientist at a small startup might have almost nothing in common with a data scientist at a massive enterprise company, for example.

So today, we decided to talk to someone who’s seen data science at both scales. Jay Feng started his career working in analytics and data science at Jobr, which was acquired by Monster.com (which was itself acquired by an even bigger company). Among many other things, his story sheds light on a question that you might not have thought about before: what happens to data scientists when their company gets acquired?

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’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).

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. 

You import your data. You clean your data. You make your baseline model. 

Then, you tune your hyperparameters. You go back and forth from random forests to XGBoost, add feature selection, and tune some more. Your model’s performance goes up, and up, and up.

And eventually, the thought occurs to you: when do I stop?

Most data scientists struggle with this question on a regular basis, and from what I’ve seen working with SharpestMinds, the vast majority of aspiring data scientists get the answer wrong. That’s why we sat down with Tan Vachiramon, a member of the Spatial AI team Oculus, and former data scientist at Airbnb. 

Tan has seen data science applied in two very different industry settings: once, as part of a team whose job it was to figure out how to understand their customer base in the middle of a the whirlwind of out-of-control user growth (at Airbnb); and again in a context where he’s had the luxury of conducting far more rigorous data science experiments under controlled circumstances (at Oculus). 

My biggest take-home from our conversation was this: if you’re interested in working at a company, it’s worth taking some time to think about their business context, because that’s the single most important factor driving the kind of data science you’ll be doing there. Specifically: