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OpenAIOpenAISan Francisco, CA

Data Scientist, GTM Intelligence

Data Scientist building GTM intelligence systems at OpenAI. Own roadmap, feature datasets, decision models (heuristic/ML/ranking), production SQL/Python pipelines, monitoring, and stakeholder alignment to drive account prioritization, risk detection, interventions, and outcome measurement for customer-facing teams.

290k – 340k/yr
Hybrid5+ YOEData Science

About the role

Responsibilities

  • Set the roadmap and methodology for GTM intelligence and decision products, using deep stakeholder discovery to probe beyond stated requests, uncover underlying decisions, workflows, constraints, and measures of success, and translate them into measurable systems.
  • Own the full lifecycle of intelligence products, including feature definition, methodology, evaluation, SQL and Python pipelines, scheduled refresh, serving, versioning, monitoring, and history.
  • Build canonical feature datasets across product telemetry, commercial systems, CRM data, customer context, and field activity.
  • Choose appropriately among heuristics, weighted scores, statistical models, ranking approaches, and machine-learning methods based on the decision, data maturity, and operational constraints.
  • Partner closely with Technical Success and other GTM stakeholders as design partners: digging into their workflows, testing assumptions, and shaping the right solution to improve account prioritization, identify risks and opportunities, select interventions, and measure outcomes.
  • Define the exposure, action, feedback, and outcome data needed to evaluate and continuously improve GTM intelligence products.
  • Create monitoring for data quality, freshness, system behavior, threshold performance, adoption, and drift.
  • Help shape trustworthy consumption layers and machine-readable interfaces for Field Insights, reporting, alerts, and agent workflows.
  • Personally ship and operate reliable first versions, partnering with Analytics Engineering and Data Engineering when work requires shared infrastructure, complex ingestion, or greater scale and reliability.

Requirements

  • Significant experience in applied Data Science, analytics engineering, machine learning, or a related quantitative role, including direct ownership of production decision systems.
  • Advanced SQL and strong production Python experience, including testing, modularity, monitoring, and maintainability.
  • Demonstrated success taking a score, signal, recommendation, ranking model, or decision rule from prototype into monitored production use.
  • Experience with feature engineering, pragmatic model selection, evaluation design, calibration or threshold setting, and ongoing system monitoring.
  • Experience building or owning reliable data transformations, canonical datasets, scheduled workflows, and application-facing outputs.
  • Strong stakeholder discovery and communication skills, including the ability to uncover the need behind a stated request and align technical and GTM stakeholders around requirements, methodology, ownership, and tradeoffs.

Nice-to-Haves

  • Experience with Databricks, Spark, dbt, Airflow or comparable orchestration, and modern cloud warehouses or lakehouses.
  • Experience with B2B SaaS, usage-based products, CRM or Salesforce data, customer lifecycle systems, recommendations, or next-best-action products.
  • Familiarity with model and feature versioning, scheduled scoring, monitoring, reproducibility, and safe rollout.
  • Experience defining exposure, action, feedback, and outcome data for decision products, experimentation, or impact measurement.
  • Familiarity with agentic systems and data interfaces designed for both human and machine consumption.

Skills

SQLPythonMachine Learningfeature engineeringData ModelingDatabricksSparkdbtAirflowCRMSalesforce

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