{"id":2199674,"url":"https://alion.io/job/temporal-senior-data-scientist","title":"Senior Data Scientist","company":{"id":4236,"name":"Temporal","domain":"temporal.io","url":"https://alion.io/company/temporal","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":12,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":137600,"max":220000,"currency":"USD","period":"year","gross":null,"usd_annual":220000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"BigQuery","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Presto","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Trino","optional":false}],"status":"live","first_seen_at":"2026-10-09T18:17:01Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-10T23:10:17Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About the Role\nThe Data Scientist, Product Growth and Experimentation will help drive decision making in support of our Product Led Growth function.\nThis 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.\nYou 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.\nThe 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.\nWhat You’ll Do\nAct as an embedded Data partner to Product, Design, and Engineering, helping shape strategy, product proposals, and experiments from the earliest stages.\n\nBuild analytical frameworks across the full PLG journey from acquisition and activation through engagement, adoption, expansion, monetization, and durability\n\nUse funnel, cohort, journey, and sequence analysis to identify friction and opportunities for improvement.\n\nIdentify 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.\n\nAnalyze feature discovery and adoption patterns to recommend interventions that increase successful adoption and expansion.\n\nDesign 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.\n\nClearly distinguish observed results from assumptions, quantify uncertainty, and explain the confidence behind each conclusion.\n\nHelp prioritize growth opportunities based on potential impact, confidence, effort, and measurement feasibility.\n\nBuild reusable datasets, metrics, dashboards, and analytical tools that reduce ad hoc work and make insights accessible to Product, Marketing, Sales, and leadership.\n\nPartner with Engineering and Data Engineering to improve event instrumentation, data quality, and the analytical models required for reliable product analysis.\n\nCommunicate findings through clear visualizations, written narratives, and practical recommendations for technical and non-technical audiences.\n\nWhat You’ll Focus on First\nDuring your first several months, you will help the team:\nAudit signals currently captured in the onboarding flow and recommend new instrumentation as needed\n\nEstablish reporting and repeatable analysis for PLG experiments.\n\nAnalyze user behavior after the first successful Activity or Workflow.\n\nIdentify signals of production readiness and common points where users stall.\n\nMap feature-adoption paths across important product areas.\n\nWhat You’ll Bring\nA strong foundation in statistics, experimental design, causal reasoning, and quantitative analysis.\n\nExperience using product, behavioral, or event data to improve activation, engagement, retention, conversion, or expansion.\n\nExperience working with small samples, noisy signals, imperfect control groups, or environments where standard A/B testing is not always possible.\n\nStrong judgment in choosing methods that fit the decision, available data, and level of uncertainty.\n\nProficiency in SQL and Python for analysis, modeling, and automation.\n\nExperience with cohort analysis, funnel analysis, segmentation, predictive modeling, and supervised or unsupervised machine-learning methods.\n\nAbility to prototype quickly in notebooks and convert recurring analyses into reliable data products, models, or workflows.\n\nExperience with modern data platforms and query engines such as Athena, Presto/Trino, BigQuery, or Snowflake.\n\nFamiliarity with cloud and object-store technologies such as AWS S3.\n\nExperience building dashboards and reusable analytical assets in a modern business intelligence platform.\n\nComfort working with evolving definitions, incomplete instrumentation, and trade-offs between analytical precision and decision usefulness.\n\nA results-oriented mindset and a record of translating analysis into product or business action.\n\nStrong communication skills, including the ability to explain methods, uncertainty, and recommendations in plain language.\n\nCuriosity about developer platforms, cloud infrastructure, usage-based products, and how customers adopt technically complex products.\n\nA collaborative approach and the ability to work effectively with Product, Engineering, Marketing, Sales, Finance, and Data partners.\n\nTemporal 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.\nTemporal is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. 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