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Eugene Mandel, Head of Product at Superconductive Health, recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science.

Mandel’s previous leadership roles within data engineering, product, and data science teams at multiple companies provides him with a unique perspective when identifying and addressing potential tension points.

Mandel’s practical experience within these roles has provided him with a unique perspective about the state of collaboration within data science as well as “why” some potential cross-team tension points arise.

Mandel also speculates that one of the potential trends we will see for the industry is that “data science will become more like data product development and be a core part of the product organization.” This blog post provides some excerpts from the discussion, the audio recording, as well as a full written transcript of the conversation.

This post is a part of an ongoing series where I sit down with various members of the industry and capture their different perspectives regarding the current state of collaboration within data science, collaboration tension points, and how to address them.

The intention of this series is to contribute to public discourse about cross-team collaboration within data science in order to accelerate data science work.

Mandel mentioned that he started noticing how data science teams were making a transition from being advisors to building data products such as a recommendation engine “When you see how data science teams made this transition from a purely advisory role, to product building role, you see how product management principles apply.

“ Mandel also later relayed a key difference between “normal software development” and developing a data product “The last company I worked for was a very interesting story as well, because I joined the company that had a very good established culture of software development…with tests, with CI/CD, experienced engineers, experienced engineering management, and product management.

And that actually was a model that we’ve followed and that was quite successful.” When asked to generalize an example and unpack a particularly thorny issue for product managers to consider while working with data scientists, Mandel relayed “so how do product managers work with data scientists?

Because when you prototype a regular software product, we’ve probably talked about user stories, use cases, you’ve started doing some kind of prototype of UI, bringing it to people and getting feedback.

But then when it transitions from prototyping stage to actual product, you see that the data set that you do have, is much less reliable, much more sparse, and when recommendations created from this data showing users, well, they’re not exactly magical.

And product managers that don’t have experience in data product management tend to make this mistake.” During the discussion, Mandel also reinforced how a data science work is probabilistic in nature and how his could lead to a tension point, particularly with engineering “Now about collaboration between engineers and data scientists… what I’ve seen is, in regular software engineering, and this is probably over generalization, but things to tend to be “true” or “not true”.

When you look at unit tests, integration tests, for software products, all that sourced …there are things like: the value is five, and this is true, and this is false, and the length of this list is 573.

When dealing with data products, you move from domestic world to probabilistic world, which means that what you expect are ranges and then you have to judge what makes sense, what doesn’t make sense.

Because we know it’s [the classifier] working, one is some kind of different process.” Mandel also relayed insight “….when talking about difference between normal software engineering and data products software engineering is [also] talking about risk.

And this kind of, you know, what we’re trying to do is making data sets first class objects, first class citizens of the data world that can be testable, describable, that you can talk about.” These excerpts are just a few insights pulled from the recent discussion at Domino HQ regarding cross-team collaboration within data science.

When data science started getting the label “data science”, probably seven years ago, I started realizing that’s a big part of what I’ve been doing.

And that really triggered me thinking about, well, “what’s different?” I think what’s interesting is that many data science teams in companies, many data science projects, and probably the many careers of data scientists, started from being in an advisory role.

Let’s say if we’re talking about some kind of marketing use case… data science can produce a recommendation that addresses “what’s the best way to talk to a particular group of users?” And that’s great.

Ann Spencer: When you were talking about… how you were noticing how data scientists were moving to an advisory kind of role and then the advice becomes your product as well as how the data scientists are coming from all these different backgrounds, physicists, psychologists, whatnot….

In every company I worked for…it was a really interesting story of evolution….for example, at Jawbone, the data science team started as producing data stories and interesting insights and eventually…well, eventually moved to owning a piece of product.

Because if you are telling an engineer, you know, if you’re not engineer and you are telling an engineer “I’m just gonna push stuff into your well guided product…you have testing, you have CI/CD, I don’t.” Then, you know, you can’t really expect a similar acceptance.

The last company I worked for was a very interesting story as well, because I joined the company that had a very good established culture of software development…with tests, with CI/CD, experienced engineers, experienced engineering management, and product management.

Because when you prototype a regular software product, we’ve probably talked about user stories, use cases, you’ve started doing some kind of prototype of UI, bringing it to people and getting feedback.

But then when it transitions from prototyping stage to actual product, you see that the data set that you do have, is much less reliable, much more sparse, and when recommendations created from this data showing users, well, they’re not exactly magical.

When you look at unit tests, integration tests, for software products, all that sourced …there are things like: the value is five, and this is true, and this is false, and the length of this list is 573.

When dealing with data products, you move from domestic world to probabilistic world, which means that what you expect are ranges and then you have to judge what makes sense, what doesn’t make sense.