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AI-Enabled Revenue Operations Specialist; F​/M​/D

in 80331, München, Bayern, Deutschland
Unternehmen: NavVis
Vollzeit position
Verfasst am 2026-09-20
Berufliche Spezialisierung:
  • Wirtschaft
    Geschäftsintelligenz, Business Analyst, Daten Analyst, AI Künstliche Intelligenz
Gehalts-/Lohnspanne oder Branchenbenchmark: 90000 - 120000 EUR pro Jahr EUR 90000.00 120000.00 YEAR
Stellenbeschreibung
Stellenbezeichnung: AI-Enabled Revenue Operations Specialist (F/M/D)

AI-Enabled Revenue Operations Specialist (F/M/D) THE OPPORTUNITY

The way B2B revenue teamsoperateisfundamentally reshaped byAIand most organizationshaven'tcaught up yet. The ones that move nowwon'tjust be more efficient;they'llsee things their competitorscan'tsee, act on signals others miss, and compound that advantage quarter after quarter.

We'rebuilding a Revenue Operations function designed for this moment. Not a back-office reporting team, but an operational intelligence engine that sits at the heart of how we generate, convert, and grow revenue. This role is central to that ambition.

As our AI-Enabled Revenue Operations Specialist, you will own three interconnected missions:

  • Revenue Insights & Reporting: Build AI-enhanced analytics across the full funnel - pipeline health, conversion dynamics, Q  narratives, revenue forecasting - turning fragmented data into decisions that leadership and frontline teams act on weekly, not quarterly.
  • Workflow Design & Optimization: Redesign how our revenue teamsactually work together
    -from lead-to-opportunity handover (BDS to AE),to deal-to-customer transition (AE to CSM), to strategic account intelligence and action.

    Eliminatethe friction and information loss thatslowdeals down and erode customer experience.
  • GTM Intelligence from Conversational & Behavioral Data: Analyze insights from customer and sales conversations (via conversation intelligence tools) and product usage patterns to surface what resonates by industry, customer segment, or buyer persona - and translate those patterns into actionable directives for GTM strategy, sales plays, and campaigns.

This is not a data engineering role and not a traditional reporting seat. Your primary job is to understand business problems across the end-to-end revenue lifecycle - where leads stall, where handovers lose context, where customer signals go unnoticed - and then build practical, AI-powered solutions that give our revenue teams an unfair advantage.

HOW YOU WILL MAKE AN IMPACT

Revenue Insights & Reporting

  • Build and continuously improve funnel reporting across BDS, AE, and CSM stages, surfacing conversion rates, pipeline velocity, stage duration, and drop-off patterns that prompt action, not just observation.
  • Prepare and automate Q  reporting packages that synthesize performance data, trends, and forward-looking indicators intonarrativesleadership can act on.
  • Create revenue and bookings reports that connect pipeline activity to financial outcomes, supporting forecasting accuracy and resource allocation decisions.
  • Use AI to accelerate insight generation - from automating data pulls and trend detection to generating draft narratives and anomaly alerts that would otherwise take days of manual analysis.

Workflow Design & Optimization

  • Map, diagnose, and redesign the end-to-end lead-to-opportunity handover process between BDS and AE teams,eliminatinginformation loss and reducing time-to-first-action.
  • Design the deal-to-customer transition workflow from AE to CSM, ensuring account context, commitments, and expansion signals transfer cleanly into the post-sale motion.
  • Build strategic account insight packages - AI-assisted briefs that combine CRM data, interaction history, usage patterns, and external signals into a single, actionable view for account teams.
  • Collaborate with adjacent teams (Marketing, Customer Support) where workflows intersect, while keeping the primary focus on the BDS - AE - CSM spine.
  • Leverage conversation intelligence tools (for example, Fathom, Gong, or similar) toidentifypatterns in buyer objections, competitive mentions, feature requests, and messaging effectiveness, broken down by industry, segment, or deal stage.
  • Combine conversational insights with product usage and behavioral data…
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