Senior Data Scientist

Overview

Embedded data scientist for Temporal's Product Led Growth function, shaping decisions across the user journey from initial interest to full adoption, with a focus on onboarding friction, adoption behaviors, and experiment evaluation.

What you'll do

• Act as an embedded Data partner to Product, Design, and Engineering, helping shape strategy, product proposals, and experiments from the earliest stages.
• Build analytical frameworks across the full PLG journey from acquisition and activation through engagement, adoption, expansion, monetization, and durability.
• Use funnel, cohort, journey, and sequence analysis to identify friction and opportunities for improvement.
• Identify the behaviors and milestones that predict conversion, production readiness, expansion, churn, and long-term customer success, and translate them into leading indicators for successful or stalled accounts.
• Analyze feature discovery and adoption patterns to recommend interventions that increase successful adoption and expansion.
• Design and evaluate experiments and product changes, using matched comparisons, pre/post-intervention analysis, causal methods, and other appropriate techniques when conventional A/B testing is not practical.
• Clearly distinguish observed results from assumptions, quantify uncertainty, and explain the confidence behind each conclusion.
• Help prioritize growth opportunities based on potential impact, confidence, effort, and measurement feasibility.
• Build reusable datasets, metrics, dashboards, and analytical tools that reduce ad hoc work and make insights accessible to Product, Marketing, Sales, and leadership.
• Partner with Engineering and Data Engineering to improve event instrumentation, data quality, and the analytical models required for reliable product analysis.
• Communicate findings through clear visualizations, written narratives, and practical recommendations for technical and non-technical audiences.

What you'll need

• A 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.
• Experience working with small samples, noisy signals, imperfect control groups, or environments where standard A/B testing is not always possible.
• Strong judgment in choosing methods that fit the decision, available data, and level of uncertainty.
• Proficiency in SQL and Python for analysis, modeling, and automation.
• Experience with cohort analysis, funnel analysis, segmentation, predictive modeling, and supervised or unsupervised machine-learning methods.
• Ability to prototype quickly in notebooks and convert recurring analyses into reliable data products, models, or workflows.
• Experience with modern data platforms and query engines such as Athena, Presto/Trino, BigQuery, or Snowflake.
• Familiarity with cloud and object-store technologies such as AWS S3.
• Experience building dashboards and reusable analytical assets in a modern business intelligence platform.
• Comfort working with evolving definitions, incomplete instrumentation, and trade-offs between analytical precision and decision usefulness.
• A results-oriented mindset and a record of translating analysis into product or business action.
• Strong communication skills, including the ability to explain methods, uncertainty, and recommendations in plain language.
• Curiosity about developer platforms, cloud infrastructure, usage-based products, and how customers adopt technically complex products.
• A collaborative approach and the ability to work effectively with Product, Engineering, Marketing, Sales, Finance, and Data partners.

Details

• Location: United States (Remote).