Compensation The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $290K - $340K per year, with generous equity, performance-related bonuses, and the following benefits: Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit) 401(k) retirement plan with employer match Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks) Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time Mental health and wellness support Employer-paid basic life and disability coverage Annual learning and development stipend to fuel your professional growth Daily meals in our offices, and meal delivery credits as eligible Relocation support for eligible employees Additional taxable fringe benefits, such as charitable donation matching and wellness stipends 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. 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. Responsibilities 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. 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. 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.