About the Team
The GTM Intelligence Solutions team builds the data and decision systems that help customer-facing teams take the right action at the right time. We combine 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 what happens next.
We’re looking for a Data Scientist to help build the next generation of GTM intelligence at OpenAI. You will own a flexible portfolio of high-impact decision data products and work closely with Technical Success and other GTM teams to ensure the work drives better decisions.
About the Role
As a Data Scientist on GTM Intelligence Solutions, you will define and build the intelligence systems that help customer-facing teams prioritize accounts, identify risks and opportunities, choose interventions, and understand what worked.
You will set the roadmap and methodology, build canonical features, ship reliable production workflows, monitor quality and adoption, and improve the systems using field feedback and business outcomes.
This role combines hands-on technical depth with strong product and business judgment. You should be as comfortable writing production Python and advanced SQL, defining durable data contracts, and operating decision products as you are evaluating a ranking approach or designing an experiment. You will personally ship reliable first versions and partner with Analytics Engineering and Data Engineering when work requires shared infrastructure or additional scale.
In This Role, You Will
Set the roadmap and methodology for GTM intelligence and decision products, using deep stakeholder discovery to probe beyond stated requests, uncover the 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 without owning the application experience end to end.
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.
Have shipped and operated model-backed or rules-based decision products, not only analyses and offline prototypes.
Are exceptional in SQL and strong in production Python, including testing, modularity, monitoring, and maintainability.
Enjoy rolling up your sleeves to move from source data through a production decision product without waiting for a separate team to complete every step.
Can independently define the problem, ask incisive follow-up questions, challenge assumptions constructively, propose the methodology, establish evaluation standards, and bring stakeholders toward decisions.
Can move between feature engineering, applied modeling, data-product design, stakeholder discovery, and production troubleshooting.
Know when to use a pragmatic approach now while designing the data and feedback foundation for more sophisticated modeling later.
Prefer measuring success through reliable adoption and better decisions, not the number or sophistication of models produced.
Qualifications
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.
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.
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.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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