Salesforce is looking to build a world-class data science and analytics platform to support and scale data-driven product decision-making, particularly within Slack, to drive innovation and customer success through AI.
Requirements
- 5+ years of experience in data science or quantitative analysis, preferably in technology product development or enterprise software.
- 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).
- Expertise in Apache Airflow is a strong plus.
- Experience designing and implementing advanced data models with scalability and efficiency.
- Experienced in applying advanced quant measurement techniques in a product development environment.
- Strong communication skills. Able to translate complex technical results into clear, actionable insights.
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.
- Partner with Product, Design, and Engineering (PDE) to contribute directly to product development—shaping what we build, how we measure success, and when/how we iterate and improve.
Other
- A related technical degree required.
- Cross-functional collaboration and influencing skills, with a track record of impacting decisions at both strategic and executional levels.
