Emily Riederer @ Capital One | Explicit design at the start of a project | Data Science Hangout

We were recently joined by Emily Riederer, Senior Manager - Customer Management Data Science & Analytics at Capital One.

We discussed how a strong foundation in high quality data infrastructure and reproducible tools sets the stage for innovation in modeling, causal inference, and analytics, and so much more.

Diving into a question asked at (42:38): What is your thought process for solving a problem that you don’t know how to solve immediately?

One thing that I think is a really undervalued part of that process is thinking about how you will know a good solution when you find one? Also, how would you know if there was a good solution staring you in the face and you already had it?

I think the more unstructured and complicated a problem can be, it can almost be a little deceptive of what's good-- which can have one or two bad outcomes.

You find a good solution, but you don't realize it's good so you keep going
You spend a lot of time chasing after an outcome, and only then do you realize, I solved the problem I was trying to solve but it wasn’t the problem I wanted to solve.

Something I've really been experimenting with in my own work is having a lot more of an explicit design stage at the beginning of a project and thinking, how can you do a pilot?

If I'm trying to predict some target, can I take those two values of that target and plug them into a downstream problem I actually thought that I was going to solve, and make sure that's actually what I want to solve?

Almost like frontloading model evaluation with even a fake solution is the first step versus last step.

Then, I'll check on one other point.

I think the other aspect of that – going back to that level of abstraction – is figuring out how to take the context out of my problem to make it something more Googleable.
So I mean thinking, not being like, “oh, this experiment, the random seeds were wrong, so I don't have a control population - what do I do?”

Backing that into more of a general question - “how do you sample a synthetic control through observational data?” which is something you can Google and then find a ton of resources about.

I think pushing myself on what I want, and then finding the right framing at which to ask for help.

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[keithgoddard]

These videos don't get the views they deserve.... I'm so grateful that I found this channel, it has been super interesting and informative (from a web dev guy transitioning to data science and R)