Data Scientist, GTM Intelligence

Details

About the Team

The GTM Intelligence Solutions team builds data and decision systems that help customer-facing teams take the right action at the right time. The team combines product telemetry, commercial data, customer context, and field activity to identify account health and opportunity, recommend actions and use cases, deliver intelligence through field-facing products and agent workflows, and measure outcomes.

The Data Scientist will help build the next generation of GTM intelligence at OpenAI, owning a flexible portfolio of high-impact decision data products and working closely with Technical Success and other GTM teams to improve decision-making.

Responsibilities

• Set the roadmap and methodology for GTM intelligence and decision products by conducting stakeholder discovery, uncovering underlying decisions, workflows, constraints, and success measures, and translating 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.
• Select among heuristics, weighted scores, statistical models, ranking approaches, and machine-learning methods based on the decision, data maturity, and operational constraints.
• Partner with Technical Success and other GTM stakeholders to improve account prioritization, identify risks and opportunities, select interventions, and measure outcomes.
• Define 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 without owning the application experience end to end.
• 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.
• Experience shipping and operating model-backed or rules-based decision products rather than only analyses and offline prototypes.
• Ability to move between feature engineering, applied modeling, data-product design, stakeholder discovery, and production troubleshooting.
• Ability to use pragmatic approaches while designing the data and feedback foundation for more sophisticated modeling later.

Preferred Qualifications

• 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.

Benefits

• Base salary of $290,000–$340,000 per year, plus equity.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for health, dependent care, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, company holidays, office closures, and paid sick or safe time as required by law.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.
• Additional benefits may include charitable donation matching and wellness stipends.

OpenAI is an equal opportunity employer and is committed to providing reasonable accommodations to applicants with disabilities. Background checks will be administered in accordance with applicable law.