Data Scientist, GTM Intelligence
$290,000–$340,000 year
RemoteUnited States or San Francisco, California, United States
Job Summary
Set the roadmap and methodology for GTM intelligence and decision products by probing stakeholder workflows, uncovering underlying constraints, and translating needs into measurable systems. Own the full lifecycle of intelligence products, including defining canonical features, building SQL and Python pipelines, and operating reliable production workflows with scheduled refreshes and versioning. Build feature datasets across product telemetry, commercial systems, CRM data, and field activity while choosing appropriate heuristics, statistical models, or machine-learning methods based on data maturity. Partner with Technical Success to shape trustworthy consumption layers and machine-readable interfaces for Field Insights, reporting, and agent workflows, ensuring continuous improvement through defined exposure, action, feedback, and outcome data. Monitor data quality, freshness, system behavior, and adoption to drive better decisions for customer-facing teams.
Required 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
Desired 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
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