Join Temporal’s Product Led Growth function as a Data Scientist embedded in Product and Engineering. Help the company understand the user journey, identify onboarding friction, and improve adoption of Temporal.

Responsibilities:

  • Partner with Product, Design, and Engineering to shape strategy, product proposals, and experiments.
  • Build analytical frameworks across the PLG journey, from acquisition and activation through engagement, adoption, expansion, monetization, and retention.
  • Use funnel, cohort, journey, and sequence analysis to identify friction and opportunities.
  • Identify behaviors that predict conversion, production readiness, expansion, churn, and customer success.
  • Design and evaluate experiments and product changes using appropriate statistical and causal methods, including when conventional A/B testing is impractical.
  • Build reusable datasets, metrics, dashboards, and analytical tools; partner with Engineering and Data Engineering to improve instrumentation and data quality.
  • Communicate findings, uncertainty, and practical recommendations to technical and non-technical audiences.

Requirements:

  • Strong foundation in statistics, experimental design, causal reasoning, and quantitative analysis.
  • Experience using product, behavioral, or event data to improve activation, engagement, retention, conversion, or expansion, including working with small samples or imperfect data.
  • Proficiency in SQL and Python; experience with cohort and funnel analysis, segmentation, predictive modeling, and machine learning.
  • Experience with modern data platforms or query engines such as Athena, Presto/Trino, BigQuery, or Snowflake; familiarity with AWS S3.
  • Experience building dashboards and reusable analytical assets, and translating analysis into product or business action.
  • Strong communication skills and interest in developer platforms, cloud infrastructure, and technically complex products.

Initial focus: Audit onboarding instrumentation, establish repeatable PLG experiment reporting, analyze behavior after the first successful Activity or Workflow, identify production-readiness signals and points where users stall, and map feature-adoption paths.

Remote work within the United States. Equal opportunity employer.