OpenAI4 дня назад
Data Scientist, GTM Intelligence
Зарплата не указана
Полная занятостьУдалёнка
Обязанности
- 01Set the roadmap and methodology for GTM intelligence and decision products
- 02Own the full lifecycle of intelligence products including feature definition, methodology, evaluation, SQL and Python pipelines, scheduled refresh, serving, versioning, monitoring, and history
- 03Build canonical feature datasets across product telemetry, commercial systems, CRM data, customer context, and field activity
- 04Choose appropriately among heuristics, weighted scores, statistical models, ranking approaches, and machine-learning methods
- 05Partner closely with Technical Success and other GTM stakeholders as design partners
- 06Define the exposure, action, feedback, and outcome data needed to evaluate and continuously improve GTM intelligence products
- 07Create monitoring for data quality, freshness, system behavior, threshold performance, adoption, and drift
- 08Help shape trustworthy consumption layers and machine-readable interfaces for Field Insights, reporting, alerts, and agent workflows
- 09Personally ship and operate reliable first versions, partnering with Analytics Engineering and Data Engineering when needed
Требования
- 01Significant experience in applied Data Science, analytics engineering, machine learning, or a related quantitative role
- 02Advanced SQL and strong production Python experience
- 03Demonstrated success taking a score, signal, recommendation, ranking model, or decision rule from prototype into monitored production use
- 04Experience with feature engineering, pragmatic model selection, evaluation design, calibration or threshold setting, and ongoing system monitoring
- 05Experience building or owning reliable data transformations, canonical datasets, scheduled workflows, and application-facing outputs
- 06Strong stakeholder discovery and communication skills
- 07Experience with Databricks, Spark, dbt, Airflow or comparable orchestration, and modern cloud warehouses or lakehouses
- 08Experience with B2B SaaS, usage-based products, CRM or Salesforce data, customer lifecycle systems, recommendations, or next-best-action products
- 09Familiarity with model and feature versioning, scheduled scoring, monitoring, reproducibility, and safe rollout
- 10Experience defining exposure, action, feedback, and outcome data for decision products, experimentation, or impact measurement
- 11Familiarity with agentic systems and data interfaces designed for both human and machine consumption