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Forward Deployed Engineer

Pendo.io Japan·Herzliya, ישראל·en
היברידימשרה מלאהSystems IntegrationEnterprise Software

Join a predictive analytics team in a hybrid contractor role based in Herzliya, Israel. Work with customers to implement predictive models in their data environments. The role requires availability during US Eastern Time hours.

תחומי אחריות

  • Lead customer technical discovery, assessing data availability, analytics maturity, and business processes.
  • Design data models, define prediction metrics, and map customer data sources to product requirements.
  • Build and present predictive models, explaining methodology, accuracy, and activation to technical stakeholders.
  • Guide customer IT and data teams through integrations; tune model parameters and resolve data quality, configuration, and expectation issues.
  • Help customer teams turn predictions into actions and playbooks.
  • Enable customer champions and share field insights with Product and Engineering teams.

דרישות

  • At least 2 years of experience in pre-sales engineering, technical account management, or a similar customer-facing technical role in B2B SaaS.
  • Strong hands-on SQL skills, including exploring unfamiliar schemas, building data models, and validating data quality.
  • Ability to lead technical discovery and explain complex concepts to business, data, and engineering stakeholders.
  • Able to take ownership and make progress in ambiguous situations.
  • Excellent written and verbal English communication skills.
  • Based in Israel, able to work hybrid from the Herzliya office, and available to work US Eastern Time hours.

יתרון

  • Background in data, analytics, business intelligence, analytics engineering, data analysis, BI development, or data science.
  • Experience with cloud data warehouses such as Snowflake, BigQuery, Redshift, or Azure Synapse, and Salesforce data structures.
  • Familiarity with product analytics tools such as Pendo, Mixpanel, or Amplitude.
  • Exposure to machine learning concepts, including model training, evaluation, and explainability.

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