Principal Data Scientist

Slack

Chicago, IL

Full Time

Senior

231k-335k

about 1 month ago

Job Description

About the Role

We are looking for a seasoned Data Science expert to help build a world-class data science and analytics platform that supports and scales executive-level, data-driven product decision-making. As a Principal Product Data Scientist, you will work closely with data science teams and cross-functional partners to derive insights from data, communicate key findings to executives, and directly drive best practices in product strategy building and implementation.

Key Responsibilities

Apply advanced data science techniques to analyze Slack product usage patterns, identifying what's working, what's not, and opportunities for improvement.
Conduct evidence-based evaluations to determine key drivers of Slack's product growth.
Define and report key success metrics, effectively communicating insights to Slack and Salesforce leadership to enable executive level decision making and follow-ups.
Synthesize insights across different product areas and business outcomes, identifying correlations and causal relationships that drive success.
Serve as a domain expert in product data science, guiding best practices and advancing data science methodologies and operations.
Champion evidence-based decision-making, making data and insights accessible and scalable for stakeholders at all levels.

Requirements

7+ years of experience in data science or quantitative analysis, preferably in technology product development or enterprise software.
A related technical degree required; an advanced degree (MS, PhD) in a quantitative field (e.g., Mathematics, Economics, Statistics, Physics, Quantitative Psychology, Engineering, etc.) is a strong plus.
Expertise in at least one programming language for data science (e.g., Python, R).
Experience working with large-scale data technologies (e.g., Spark, Presto, Hive, Hadoop).
Strong executive communication skills, with the ability to translate complex data into clear, actionable insights.
Cross-functional collaboration and influencing skills, with a track record of impacting decisions at both strategic and executional levels in a large corporate environment.
Experience designing advanced data pipelines and schemas for scalability and efficiency.
Strong statistical and machine learning knowledge, with experience building descriptive and predictive models.
Expertise in DS measurement methodologies, with the ability to translate technical data into meaningful takeaways for non-technical stakeholders.