Principal Data Scientist, Platforms

Job Category

Products and Technology

Job Details

We are hiring a Principal data scientist to lead the data science efforts on guiding Slack platform evolution. The Platform ecosystem is a critical component of Slack’s strategy to become the place where work happens. It enables users to truly customize Slack and integrate their everyday tools to streamline and speed up work.

In this leadership role, you will work closely with the Platform product organization to understand and improve the product experience of app developers and builders - non-technical people leveraging Slack’s no-code platform tools. As Slack builds out its next generation of Platform capabilities, you’ll lead the efforts on unearthing insights and building a complete picture of what users build in Slack. You will also design and establish the foundational data infrastructure to enable measurement of various Platform tools. At the same time, you will partner closely with product researchers, designers, and engineers to uncover user experience gaps and opportunities, and influence future product roadmaps accordingly.

Success in this role requires analytical savviness, problem-solving sophistication, a willingness to roll up your sleeves and a dedication to make the highest impact possible. We are looking for someone that can think on their feet and thrives in a dynamic environment.

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?

What you'll be doing:

• Establish strong partnership with product stakeholders in identifying opportunities, prioritize roadmaps, define and implement execution excellence, and coach and mentor team members for execution excellence.
• Evangelize evidence-based decision making by partnering with key decision makers and driving general accessibility of data and insights.
• Apply various data science methods to understand usage patterns of developers and builders who create on the Slack Platform.
• Perform evidence-based evaluations of the most important drivers of increased adoption of the Platform capabilities
• Build out key success metrics and reporting for the Platform product organization.
• Prototype foundational data pipelines and partner with the data engineering organization to elevate these into canonical sources of truth for Platform metrics.
• Identify and communicate potential opportunities to improve the experience of users who want to build apps in Slack.
• Organize disparate facts and analyses into coherent narratives that evaluate the success of and influence product strategy.
• Set a compelling vision and prioritize work based on product strategies and overall goals.
• Partner closely with product researchers, align roadmaps and collaborate on projects to answer complex questions.
• Build strong partnership and alignment with our data cross-functional partners, including Data Engineering and Business Intelligence, to author and develop core data sets and empower operational and exploratory analysis.

What you should have:

• 7+ years of experience in product data science in related tech industries, 2+ years experience in a product DS managerial role a plus.
• A consistent record of using data to drive product teams to achieve ambitious goals and influencing company-level outcomes.
• BS degree in a quantitative field (e.g. Computer Science, Economics, Physics); An advanced degree (MS, PhD) in a quantitative field (e.g. Computer Science, Economics, Physics) a strong plus.
• Expertise in at least one programming language for data analysis (e.g. Python, R, Scala).
• Experience working with data technologies that allow analysis of large amounts of data (e.g. Spark, Presto, Hive, Hadoop, etc). Familiarity with Apache Airflow is a strong plus.
• Experience in schema design, data modeling, and building data pipelines.
• Experience building frameworks, standards, and other tools to simplify and automate key data science and analytics work.

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