OpenAI7 дней назад

Data Scientist, GTM

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Полная занятостьУдалёнка

Обязанности

01Define a trusted measurement framework for knowledge-worker adoption, including identity, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization
02Map the knowledge-worker journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption
03Identify which personas, functions, use cases, product capabilities, and account conditions are associated with deep and retained usage
04Design and evaluate experiments and quasi-experiments across onboarding, enablement, workflow templates, connectors, pilots, customer deployment support, and product launches
05Combine behavioral data with customer and field evidence, then translate the findings into crisp recommendations for Product, GTM, Finance, and executive audiences
06Operationalize successful work through durable datasets, scorecards, recurring business narratives, and decision cadences while partnering with Analytics Engineering and product teams to improve instrumentation and data quality

01Significant experience in data science, product analytics, growth analytics, economics, statistics, or a related quantitative field
02Strong hands-on ability in SQL and Python, including experience working with large and imperfect behavioral datasets
03Experience defining activation, retention, engagement, funnel, or product-adoption metrics, with strong knowledge of experimentation, causal inference, cohort analysis, and segmentation
04Ability to translate ambiguous business questions into structured analyses, independently define the analytical direction, and bring senior stakeholders along through clear tradeoff framing
05Strong written and verbal communication skills, including a demonstrated ability to influence senior technical and non-technical stakeholders
06Enjoy creating clarity from problems that do not yet have stable definitions, clean datasets, or a settled playbook
07Move comfortably between SQL, Python, metric design, experimentation, customer evidence, strategy, and executive communication
08Think in terms of user journeys and behavioral mechanisms—not only dashboards and aggregate metrics
09Can distinguish product usage from durable customer value and simulated value from realized business outcomes
10Proactively align stakeholders, document decisions, and surface data-quality risks before they affect important decisions
11Are comfortable challenging an attractive narrative when the evidence does not support it