Customer Success Manager

Overview

You manage a book of 8-12 enterprise accounts after they've bought Domino's platform. Your job is making sure their data scientists actually use it (adoption is never guaranteed with technical products), their platform runs smoothly, and you identify opportunities to expand into more use cases or teams. You're part project manager, part technical consultant, part account manager. You work closely with RevOps since both functions now report to the same leader.

Role Snapshot

Company Context

Stage: Later-stage (248 employees, established enterprise customer base)

Size: 248 employees

Growth: CS and RevOps now unified under same leader—suggests company is focusing on efficiency and customer-centricity

Market Position: Retention and expansion critical in competitive MLOps market where customers can churn to alternatives

GTM Reality

Your Customers:

• Large enterprises (Fortune 500 type) with 20-100+ data scientists
• Complex implementations—multiple teams, different use cases
• High expectations and sophisticated technical users

Team Structure:

• You likely have 8-12 accounts (mix of sizes)
• Technical account managers or solutions architects may support you
• You work closely with Support for technical issues
• RevOps gives you data on usage metrics, health scores

Expansion Motions:

• Adding more data science teams/business units
• Increasing platform capacity (more users, more compute)
• New use cases (expanding from model training to full MLOps)

Competitive Landscape

Churn Risks:

• Customer builds competing internal tooling
• Switches to AWS SageMaker or Databricks ("why pay for another platform?")
• Budget cuts hit "nice to have" tools first
• Champion leaves and new leader questions the investment

Retention Strategy:

• Prove continuous ROI—faster model deployment, more models in production
• Expand to become embedded in workflows (harder to rip out)
• Build relationships beyond original champion

What You'll Actually Do

Time Breakdown

Customer Meetings (30%) | Adoption/Health Monitoring (25%) | Expansion Selling (20%) | Internal Coordination (15%) | Escalation Management (10%)

Key Activities

• Quarterly Business Reviews: You run QBRs showing adoption metrics, ROI, and roadmap alignment. You're asking: "Are we solving your problems? What's next?" These uncover expansion opportunities or early churn signals.
• Adoption Tracking: You monitor usage data—which teams are active, which aren't logging in, where models are getting deployed. Low usage = churn risk. You reach out proactively when metrics dip.
• Technical Onboarding: New teams joining Domino need onboarding. You're coordinating training, best practices, integration work. This is hands-on—you're often on Zoom walking through workflows.
• Expansion Conversations: When you see opportunities (more teams, more use cases), you're positioning expansion. "Your fraud detection team is crushing it—have you thought about bringing credit risk onto the platform?" You work with AEs on expansion deals.
• Escalation Management: When customers hit technical issues or frustrations, you're coordinating with Support, Engineering, Product. You're the internal advocate ensuring their problems get fixed.
• Renewal Management: You own renewals. 90 days out, you're assessing health, addressing concerns, and ensuring renewal is smooth. No one wants surprises.

The Honest Reality

What's Hard

• Proving ROI is abstract: "We deployed models faster" is hard to quantify. Customers sometimes question value, especially if adoption is uneven across teams.
• Technical complexity: The product is complex. Customers run into infrastructure issues, integration challenges, user errors. You need enough technical depth to triage and escalate intelligently.
• Expansion selling on top of support: You're balancing "keep them happy" with "grow the account." Pushing expansion when they're frustrated about a bug is bad timing.
• Champion risk: Your main contact leaves, and suddenly you're rebuilding relationships with someone skeptical about the tool. This happens constantly.
• Renewal pressure: If you're responsible for net retention targets, renewals are high-stakes. Losing a $300K account blows your quarter.

What Success Looks Like

• You maintain 95%+ gross retention (minimal logo churn)
• You hit 110-120% net retention through expansions
• Customer health scores stay green (usage metrics, sentiment, engagement)
• You identify and close 2-3 expansion opportunities per quarter
• Customers reference you as a trusted advisor, not just a vendor

Who You're Working With

Primary Contacts:

• Data Science/ML Engineering Leaders (day-to-day champions)
• Platform Engineering (technical stakeholders)
• Original executive sponsor (check-ins, renewals, expansions)
• End users (data scientists) when troubleshooting

What They Care About:

• Champions: Am I getting value? Is my team actually using this? Can I justify the cost?
• Executives: ROI, strategic alignment, risk mitigation
• End Users: Does this make my job easier or harder? Do bugs get fixed quickly?

Requirements

• 3-5 years in technical customer success or account management (ideally with data/ML/infrastructure products)
• Ability to read usage data and translate it into action ("Team X hasn't logged in for 3 weeks—what's up?")
• Enough technical fluency to understand ML workflows and speak credibly with data scientists
• Experience managing enterprise accounts with multiple stakeholders
• Comfort with expansion selling—this isn't pure support, you own revenue targets
• Strong project management skills (coordinating onboarding, integrations, escalations)