Product Insights Manager (Data Science)

Production & Product Management San Francisco, California


Product Insights Manager (Data Science)


As a Product Insights Manager, your ultimate goal will be to generate high quality insights. Through your deep understanding of the product, combined with your analytical and statistical capabilities, you will synthesize data and product insights together into tangible actions and partner up with the Product Lead to generate inflective designs and outcomes.





        You will work directly with product and technical leaders and perform planned and exploratory analyses to provide actionable insights that drive product growth and greatness as our software continuously changes.

        You will work with massive amounts of raw data and build ETL processes and tools that increase the efficiency with which we can generate actionable insights.

        You will help design experiments (A/B or multivariate tests) and will help maintain the technical and statistical aspects of our experimentation pipelines.

        You will create data visualizations and dashboards to surface trends, distributions, and relationships between business metrics.

        You will build, test, and deploy models for personalizing and targeting user experiences. You will own these projects individually and drive them yourself as opposed to contributions across multiple projects owned by others.





        You are a competent storyteller, effective and uninhibited in communicating actionable insights to stakeholders. You are willing to engage in spirited dialogue about the findings, where you have to defend your methodology and interpretation.

        You are a competent statistician. You are capable of taking ownership of daily tasks such as generating predictions and segmentation schemes using various machine learning technologies, running MCMC simulations, or evaluating multivariate tests. You are well-versed in causal inference methodologies such as propensity score matching and regression discontinuity and know when to use them. You can tell a good instrumental variable from a bad one.

        You are a competent programmer, preferably in Python or R, fluent in SQL, and are familiar with distributed technologies (e.g. Spark). You have a spirit for aggressively automating away repeating tasks. You are capable of driving this process of automation and building tools for the rest of the team to use autonomously, while generating minimal requirements for our data engineering team.

        You are a competent scientist. Your specific background doesn’t matter nearly as much as the fact that you understand the scientific process and think deeply about causality. You generate and test causal hypotheses rigorously and robustly. You don’t jump to conclusions or confuse correlation with causation.