Newton Research

Newton Research

Solutions Engineer

New York City, USA · Senior

Sponsorship not specified$135k-$155kDetected 99 days ago
PythonSQLLLMsAgentic AIProduct ManagementMarketing AnalyticsResearchCommunicationProblem SolvingOrganizational Skills

About the role

  • The ideal candidate will have advertising technology experience and a diverse skill set encompassing growth, product, marketing, Gen AI, and customer success.

Requirements

  • 5+ years of experience in the advertising technology industry, preferably in analytics or data operations
  • Experience collaborating with clients and cross-functional teams, including product, engineering, and sales
  • Deep understanding of the analytics and AI landscape, mainly how advertisers and publishers think about use cases in analytics, attribution, and profitability
  • Experience working with CPG brands or in CPG-adjacent environments is strongly preferred; familiarity with retail media ecosystems and Amazon data feeds - including Amazon Marketing Cloud (AMC) - is a plus
  • Strong problem-solving skills with a passion for communicating and delivering technical solutions

Nice to have

  • Experience working with CPG brands or in CPG-adjacent environments is strongly preferred
  • familiarity with retail media ecosystems and Amazon data feeds - including Amazon Marketing Cloud (AMC) - is a plus
  • Understanding of SQL and Python is preferred

Skills

  • Company Description
  • Role Description
  • This engagement requires 2 days per week onsite at the client's NJ/NYC location.

Compensation

  • $135,000-$155,000

Company info

  • Identify opportunities to integrate platform usage into user workflow habits by guiding customers toward high-value use cases
  • Conduct regular check-ins with customers to understand their evolving needs and provide guidance on leveraging Newton's solutions effectively, acting as the communication bridge between customers and internal AI team

This listing is sourced directly from Newton Research's careers page and normalized into a canonical job model.