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Staff Data Scientist - Research & Analytics
Slack is hiring a staff data scientist for our Expansion product team. The Expansion team is a cross-functional team that builds foundational product experiences to help teams become successful on Slack. Our team focuses on the critical moments within the team’s lifecycle: creating and joining workspaces, in-product education, expanding the number of users on Slack, deeping feature usage & engagement, and purchasing & billing.
You will use data to help the team discover opportunities and define appropriate solutions to user problems by performing research into user behavior, defining and applying experimentation standards, and understanding the drivers of performance. You will inform and influence decisions using data and research, by crafting our learnings into narratives and partnering with key decision makers. You will be a strong partner to researchers with varying backgrounds, including user researchers, market researchers, and survey scientists.
Slack has a positive, diverse, and supportive culture—we look for people who are curious, inventive, and work to be a little better every single day. In our work together we aim to be smart, humble, hardworking and, above all, collaborative. If this sounds like a good fit for you, why not say hello?
Responsibilities:
Applying various data science methods to understanding the most important aspects of our product, users, and business.
Designing new datasets and metrics to be added to our data model.
Organizing disparate facts and findings into powerful narratives that have the broadest possible application within Slack.
Evangelizing evidence-based decision making by partnering with key decision makers and driving general accessibility of data and insights.
Identify and drive improvements to the execution of the full Research + Analytics team through standardization of process, authoring new best practices, mentorship and cultivation of new expertise.
Requirements:
8+ years of professional industry experience doing quantitative analysis.
An advanced degree (MS, PhD) in a quantitative field (e.g. Computer Science, Economics, Physics) a plus.
A consistent record of using data to drive product teams to achieve ambitious goals and influencing company-level outcomes.
Expert SQL and at least one general programming language (e.g. Python).
Experience in schema design, data modeling, and building data pipelines.
Experience with a broad set of statistical and machine learning methods to build descriptive and predictive models.
Experience with designing and testing experiments.
Experience working with data technologies that allow effective storage and analysis of large amounts of data (e.g. Spark, Presto, Hive, Hadoop, etc).
Experience working with online data systems (e.g. relational databases, logging ingestion) a plus.
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