Job description

About the Role The Data Scientist, Product Growth and Experimentation will help drive decision making in support of our Product Led Growth function. This role sits at the intersection of Data, Product, Growth, and GTM. You will work deeply embedded within the Product and Engineering teams focused on PLG. Your focus will be illuminating the user journey from initial interest to fully adopted and all the steps in between. You will surface friction points in the onboarding process and identify behaviors that are indicative of successful adoption. You will evaluate interventions that help us to bring the value of Temporal to more users. You will work effectively across both large data, clean data sets and data sets with constraints in duration and sample size to provide well-informed recommendations and conclusions. You will bring deep knowledge and practical experience with statistical methods including matched comparisons, quasi-experimental methods, and uncertainty measurements to help overcome these constraints. The ideal candidate possesses strong analytical judgment and practical product sense. Ambiguous business questions do not intimidate you. You have a keen sense for when additional data or analysis could materially change a conclusion, and when it wouldn’t. You apply analytical rigor while balancing what is functionally required to advance a project. Your aim is to drive better decisions and faster learning through measurable customer outcomes. 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 FOCUS ON FIRST During your first several months, you will help the team: - Audit signals currently captured in the onboarding flow and recommend new instrumentation as needed - Establish reporting and repeatable analysis for PLG experiments. - Analyze user behavior after the first successful Activity or Workflow. - Identify signals of production readiness and common points where users stall. - Map feature-adoption paths across important product areas. WHAT YOU’LL BRING - 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. Temporal Technologies is an Equal Opportunity Employer. Temporal Technologies does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need. We embrace and celebrate differences and diversity. Temporal is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. If you need to request a reasonable accommodation, please let your Recruiter know so we can assist.

Requirements

Must-haves

The Data Scientist, Product Growth and Experimentation will help drive decision making in support of our Product Led Growth function.
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.
During your first several months, you will help the team:
Audit signals currently captured in the onboarding flow and recommend new instrumentation as needed
Establish reporting and repeatable analysis for PLG experiments.
Analyze user behavior after the first successful Activity or Workflow.
Identify signals of production readiness and common points where users stall.
Map feature-adoption paths across important product areas.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonaws

Platform: ashby
Effort: quick