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About Anaconda
Be at the center of AI
Anaconda is built to advance AI with open source at scale, giving builders and organizations the confidence to increase productivity, and save time, spend and risk associated with open source. 95% of the Fortune 500 including Panasonic, AmTrust, Booz Allen Hamilton and over 50 million users rely on the value The Anaconda AI Platform delivers through a centralized approach to sourcing, securing, building, and deploying AI. With 21 billion downloads and growing, Anaconda has established itself as the gold standard for Python, data science, and AI and the enterprise-ready solution of choice for AI innovation. Anaconda is backed by world-class investors including Insight Partners. Learn more at .
Summary:
The Customer Success team at Anaconda is the primary relationship layer between our platform and the enterprise organizations that depend on it — from data scientists and ML engineers to IT architects and security teams managing open-source risk at scale. We help customers get real, measurable value from Anaconda 's product suite by building trust, showing up prepared, and staying curious about what each customer is actually trying to solve.
As a Senior Technical Customer Success Manager, you own a portfolio of strategic enterprise accounts and operate as the voice of the customer across the organization. You bring deep technical fluency (across Python packaging, environment management, and enterprise security and governance) and you use it to close the gap between what customers deploy and the value they actually realize. You're not here to just manage relationships. You're here to set the standard for how we engage technically and commercially, and to leave the function stronger than you found it.
What You’ll Do:
Own a portfolio of enterprise accounts. Build deep relationships across complex organizations (from data scientists and platform engineers to IT architects, security leads, and C-suite stakeholders) well beyond the initial point of contact.
Accelerate onboarding and initial adoption. Guide customers through first-use milestones: environment setup, CVE policy configuration, private repo and channel tokenization, and initial workflow adoption, coding assistant & quick start processes. Identify and resolve technical blockers before they threaten time-to-value.
Drive ongoing usage health and consumption. Monitor seat utilization, package and channel usage, and admin portal signals to identify underused licenses, stalled users, or shadow IT risk. Proactively engage the AE when usage patterns suggest expansion opportunity or churn risk.
Arrive at every customer interaction with a prepared point of view. Draw on product usage data, health scores, and technical signals to drive conversations forward and deliver clear value at each touchpoint. No filler meetings.
Own the technical inputs to account health scoring. Surface risk early (unresolved CVE exposure, blocked package access, unmanaged environments) and coordinate mitigation plans with the RM, Support, and FDE.
Act as the voice of the customer to Product and Engineering. Channel technical requirements, competitive intelligence, and enhancement requests. Partner with Solutions Engineering on where Anaconda complements or competes with adjacent platforms in the customer's stack.
Build the technical narrative for QBRs and executive engagement. Partner with the account team to document use cases, quantify value delivered (time saved, risk reduced, packages secured), and make the business case stick at the executive level.
Support renewal and expansion. Bring technical proof points into renewal conversations (usage data, security posture improvements, workshop outcomes), advise the RM on churn forecast based on adoption and usage signals, and flag technical expansion whitespace — new teams, new use cases, adjacent workloads — to the AE for commercial pursuit.
Use AI as a force multiplier. Build AI-enabled success approaches the team actually adopts. Know where AI belongs in the customer motion and where human judgment stays central. Your standard becomes the team's standard.
Contribute to playbook development and team infrastructure. Senior TSMs are expected to leave the function stronger than they found it — through documentation, engagement templates, peer coaching, and process improvements that raise overall team performance.
Mentor and coach less experienced team members through account reviews, technical strategy sessions, and informal guidance. Translate personal experience into repeatable, teachable approaches.
Operate within a GTM pod. Sit alongside the Account Executive, Renewal Manager, Solutions Engineer, and other pod roles (FDE, BDR, TAM where applicable) under a shared pod leader. Attend weekly pod and pipeline meetings and collaborate closely with the AE on account plans, customer relationships, and QBRs.
What Success Looks Like :
Net revenue retention (NRR) across your enterprise portfolio
Customer health score trends and reduction in at-risk accounts
Time-to-value and adoption depth across your book of business
Quality and completeness of success plans, technical documentation, and account records
Expansion pipeline sourced from within existing accounts
AI-enabled workflows or playbook contributions adopted by peers
What You’ll Need:
6+ years in a technical customer-facing role — customer success, technical account management, solutions engineering, or similar — with demonstrated ownership of a £4 million+ book of business and accountability for retention and growth outcomes at large enterprise accounts (10,000+ employees)
Understanding of enterprise security and governance concepts relevant to Anaconda : CVE exposure, package provenance, air-gapped and private repo deployment, SSO, and gated registration
Experience with data science, ML, or developer tooling platforms — Databricks, Domino, Dataiku, SageMaker, or similar — and familiarity with how Anaconda fits into or alongside these environments
Ability to read logs and error output to triage technical issues before escalating to Support or Professional Services — you know the difference between self-resolving, routing to Support, and escalating to Product
Proven ability to build relationships across complex organizations — from practitioners to VP and C-suite stakeholders — and translate technical risk and product capability into executive-level business impact
Familiarity with adjacent and competitive tooling — SageMaker, Databricks, Snowflake, Vertex AI — sufficient to position Anaconda 's complement-vs.-compete story credibly Project management skills to coordinate multi-stakeholder technical engagements: onboarding, workshops, proofs of concept Strong verbal and written communication across all levels of an organization, including executive audiences
AI fluency in daily work — comfortable using AI tools to research, document, prepare, and draft; rigorous about reviewing what you produce under your name before it reaches a customer
Bachelor's degree in a technical field or equivalent practical experience
What Will Make You Stand Out:
Experience at a high-growth or early-stage company where the playbook wasn't written yet — you've helped build structure from scratch, operated with autonomy, and stayed effective when priorities shifted underneath you
Working fluency in Python packaging and environment management — conda, pip, pixi, and the tradeoffs between them — and the ability to hold a substantive technical conversation without SE support in routine scenarios
Deep familiarity with open-source ecosystems — software supply chain security, dependency management, license compliance exposure, and vulnerability workflows — and the ability to speak credibly to a CISO or VP Engineering about why it matters
A history of contributing to team infrastructure — playbooks, engagement templates, peer coaching, or process improvements that raised overall team performance, not just your own numbers
Experience in a high-growth, open-source, or data science-oriented company where customer needs evolve quickly and the product is still maturing AI-enabled workflow contributions — you've built something others use, not just used AI for yourself; you've made a judgment call about where AI doesn't belong and explained why You embody our values — Curious by Default, Build Together, Own It, and Lead with Guts and Heart — and our behaviors: Clarity, Care, and Candor
Why You'll Like Working Here:
You'll thrive in a high-performance environment where results are recognized and rewarded
Your work directly contributes to shaping the future of data science, machine learning, and AI in the enterprise.
You'll work alongside a collaborative team that values diverse, thoughtful discussion, clarity and candor.
You'll be supported by a culture that puts employees first - with flexible hours, a fully remote setup, and a genuine commitment to your wellbeing and growth.
There is no application deadline; we accept applications on an ongoing basis.
An Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
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