Temporal Technologies - Senior Data Scientist

Remote - United States (Remote)$138k - $220k2w ago

Remote Senior NA Cloud Computing Nonprofit Senior Data Scientist SQL Python Product Marketing Snowflake AWS

Requirements

• 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.

Responsibilities

• 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.