About the role
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. You Might Thrive in This Role If You 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.
