Too Lost

Too Lost

A&R Manager (Asia)

New York · Contract

Sponsorship not specifiedDetected 22 days ago
Lead GenerationCold OutreachResearchCommunicationOrganizational Skills

About the role

  • You will act as a spokesperson and representative of Too Lost and engage independent labels and artists, educate them on the Too Lost Product Suite, and establish a working relationship with these prospective clients.
  • You will assist Priority clients with product management to ensure assets are ingested with technical precision.
  • Your goal is to increase the company's market share by bringing high-value artists, labels and catalogs into the ecosystem and maximizing their commercial potential post-ingestion.

Responsibilities

  • Lead Generation & Pitching: Actively scout and identify high-value independent artists, labels and catalogs currently dissatisfied with their distribution or administration.
  • Revenue Optimization: Identify and advise on key tracks within the catalog to drive mutual ROI.
  • Post-Ingestion QC: Perform Quality Control on DSPs (Spotify, Apple Music, etc.) to ensure releases are correctly linked, profiles are mapped and play counts are preserved.
  • Proven track record of success with building pipelines through cold outreach and market research
  • Ability to work independently and manage multiple projects simultaneously
  • Our distribution and publishing services deliver, monetize and protect songs across the globe for over 450,000+ musicians, record labels, studios, brands, investors, and platforms.
  • Identify and advise on key tracks within the catalog to drive mutual ROI.

Requirements

  • Ability to follow structured workflows and meet deadlines and uphold KPI metrics

Benefits

  • Pitch the benefits of Too Lost to key decision-makers and potential clients.

Company info

  • What We Are Looking For

Equal opportunity

  • Too Lost LLC is an equal opportunity employer committed to building a diverse and inclusive team.

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