Client Optimization Specialist - AI Technologies, Excel - Finance market

2+ years
Long-term (40h)
Finance
Full Remote
Amazon QuickSight
Excel
Metabase
AI Technologies

Requirements

Must-haves

  • 2+ years of professional experience focused on data
  • Experience with data and analytics tools (e.g., Quicksight, Excel, Metabase)
  • Proficiency with systems and integrations
  • Ability to collect, analyze, and interpret complex data
  • Deep knowledge of AI tools and ability to apply them for workflow automation and efficiency gains
  • Excellent presentation skills
  • Strong problem-solving skills
  • Strong communication skills in both spoken and written English
  • Bachelor’s Degree in Data Science, Statistics, Computer Engineering, Computer Science, or equivalent

Nice-to-haves

  • Startup experience

What you will work on

The Client Optimization Specialist is a back-end technical professional focused on driving process improvements. Collaborating closely with Client Account Managers (CAMs), this role ensures customer success with the platform.

  • Responsibilities include delivering customer-requested reports, optimizing internal workflows, developing system integrations, and enhancing overall efficiency and customer experience.
  • Audit business systems, workflows, and data flows to identify inefficiencies and implement automated solutions (including AI) to improve data quality and streamline processes
  • Provide reporting and system support to Client Account Managers (CAMs), while developing playbooks and automating insights
  • Design, build, and maintain integrations between internal systems and third-party platforms to enable seamless data exchange and operational efficiency
  • Map and improve workflows by documenting best practices and training team members on updated processes
  • Leverage AI and emerging technologies to maintain and expand customer documentation (help articles, tutorials, FAQs) in partnership with the Support team
  • Act as a subject matter expert for business systems, troubleshooting complex issues and serving as a connector between Customer Success, Product, and Engineering