Uncountable Inc

Uncountable Inc

Customer Success Associate

London/New York · Junior

Sponsorship not specified$30k-$40kDetected 80 days ago
Account ManagementExcelCustomer SuccessLeadershipMentoring

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odds of building a lasting career here

37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level I)39
Fits your clock70

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About the role

  • Enterprise software succeeds or fails on adoption.
  • You'll be the person who gets scientists actually using Uncountable:
  • This is a specialist role, deployed where it matters most.

Responsibilities

  • Create the adoption plan for each of your accounts: who needs to be using what, by when, and what's blocking them today
  • Diagnose why adoption stalls (unclear workflows, missing configuration, change resistance, an unconvinced manager) and drive the fix with the right internal team
  • Partner with the Implementation team late in each project to lead go-live training, then own the enablement relationship from there
  • Build and run training tailored to each audience's actual workflows rather than generic feature tours, for scientists, lab technicians, data managers, and R&D leadership
  • Develop customer-side champions and power users who sustain adoption after you step back
  • Create reusable enablement assets that scale beyond the accounts you touch directly
  • Own rollout and adoption of Bodie, Uncountable's AI assistant, across your accounts
  • Teach scientists how to use AI on their own data, moving them from curiosity to daily use
  • Run working sessions with R&D teams, translating how they design experiments and record results into how they should use the platform day to day

Requirements

  • 1 to 4 years of experience in customer success, enablement, technical training, implementation, consulting, or a scientific role with meaningful customer exposure.
  • A degree in chemistry, materials science, biology, chemical engineering, or a related field, or equivalent hands-on experience in a scientific or technical environment.
  • Comfort with data and software.
  • Clear writing, clear speaking, and the ability to adjust between a bench scientist and an R&D director.
  • Ability to travel 30 to 40% within your region.

Nice to have

  • Hands-on lab experience.
  • Exposure to data management concepts: structured spreadsheets, databases, ELNs, or LIMS
  • Interest in AI and how it's changing R&D workflows
  • The highest-leverage problem we have.
  • Deployed where it counts.
  • First of its kind here.
  • You'll be our first dedicated Customer Success Associate, working from an established implementation and account management methodology while bringing a sharper focus on training and adoption than we've had before
  • A front-row seat on AI in R&D.

Skills

  • Science moves slowly, not because researchers aren't brilliant, but because their tools haven't kept up.
  • Uncountable is changing that.

Compensation

  • $30k-$40k

Company info

  • The highest-leverage problem we have. Adoption is the difference between a platform customers renew out of habit and one they can't operate without
  • Deployed where it counts. You'll work our largest and most strategic accounts, alongside our most experienced AMs and implementation leads
  • First of its kind here. You'll be our first dedicated Customer Success Associate, working from an established implementation and account management methodology while bringing a sharper focus on training and adoption than we've had before
  • A front-row seat on AI in R&D. You'll be the person teaching working scientists at global enterprises how to actually use AI in their labs
  • Direct impact. Your work accelerates R&D at organizations developing next-generation products, from sustainable materials to pediatric cancer treatments
  • An engineering-first culture. You'll work closely with our engineering and product teams, giving you a window into how world-class software gets built and a voice in shaping it

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