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Should you ship this feature?

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Introduction During my time at tech companies, one of the hardest tasks for my team is to guide the Product team in their decision to launch a feature. It is both very consequential and also very fraught with pitfalls. Let's take an example to ease into the topic: You worked hard to convince leadership to measure the effect of this cool new feature and you got a nice experiment set up according to the cannons of measurement. After 4 weeks, you are seeing stat sig improvement in your primary metric and not secondary metrics affected negatively. The story is clear as day: you should tell the team to ship it, right?  The problem with shipping features Once the results are out, the focus is put on the outcomes and leadership is incentivized to ship. As a result, little attention is given to cost side of the equation: Tech debt : Does this feature make the system more complex? Does it hinder long term growth by exacting a tax from any new feature? Think about Net Prevent Value of such t
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My new job at Lyft

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In October, I joined Lyft as Data Science Manager for Core Mapping. I wish I could have posted this update a while ago but a big event got in the way (yes, we went public) ... While low visibility, Mapping turns out to a big deal for ride-sharing as it has influence on a lot of other services. In a nutshell, Mapping has an impact on pricing, driver dispatch, scheduled rides and customer XP. Also, it is usually the biggest friction point for seamless pickups. Why is Mapping important for ride sharing company ? Mapping has traditionally 4 components: Basemap (representation of the world as a graph), Locations (where are drivers, passengers ?), Routing (optimal paths between locations) and ETA (distance and time between locations). When you open your app, you see the following screen: A lot of information displayed is connected to the work by my team: The surrounding physical world : road segments, (train) stations, POI The pick-up ETA (3min) looks at drivers around t
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5 rules for a productive Science team

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Data Science is a new discipline. As such, companies (incl. Tech) are still trying to figure out the optimal configuration for the team. However, there are a few guiding principles that are important to follow so that your Science team does not fall apart. I have complied my own in this blog article and I'm sharing them with you to generate a discussion: 1- Have a single source of truth During my consulting years, I cannot recall how many of my clients had challenges that emerged largely from a single problem: teams looking at different data.  Aligning on metrics and methodology is very important as it forces the team(s) into a "single view of the world". Without agreement, team progress is hampered due to definition conflict, and confusion (when metrics disagree). In practice, this can be achieved through a single "fact table", adequate documentation (with thorough definition and pointers to code) and availability of metrics where they matter
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