СШАSenior Customer Success Manager
4 нед. назад
170k–190k USD / годСШАSeniorHybrid
data integrationcustomer relationship managementtechnical deployment
Manage technical deployment and integration of AI platform for enterprise customers, ensuring functional systems that meet requirements and foster long-term relationships.
О компании
- is the pioneering Causal AI platform. We help the world's largest enterprises move past correlation to prove what actually drives business outcomes — the question marketing and growth teams have never been able to answer with confidence. Fortune 100 companies including Nvidia, Delta Air Lines, and Mars use to make multimillion-dollar decisions on trusted, causal evidence.
- We're backed by a $145M Series B from WndrCo (founded by Jeffrey Katzenberg), Jensen Huang, Joe Montana, Prysm Capital, and Accenture. Our models run on our own NVIDIA DGX SuperPOD built on Grace Blackwell infrastructure — one of the fastest private supercomputers in the world.
Обязанности
- We're hiring a Senior Customer Success Manager in Chicago to own the technical deployment and integration of the platform for our enterprise customers. You'll turn sales promises into technical reality: a fully functional system that meets the customer's requirements and sets the foundation for a long relationship.
- Causal analysis is only as good as the data going into it, so the quality of your implementation directly determines whether a Fortune 100 marketing organization trusts what our models tell them. Time-to-value is the number that matters, and you'll own it.
- The seat is in Chicago because a concentration of our most strategic enterprise accounts is here. Kickoffs, data workshops, and go-live moments go materially better in person, and you'll be close enough to do them that way.
- Lead implementation projects for enterprise B2B SaaS customers, ensuring smooth onboarding and successful adoption of 's platform.
- Develop and maintain implementation plans, timelines, and resourcing so projects land on or ahead of schedule.
- Ensure data integrations function correctly and meet customer requirements — the integrity of every downstream causal result depends on it.
- Create documentation comprehensive enough to enable real knowledge transfer, internally and to the customer.
- Act as the liaison between customer success, engineering, and product management to keep everyone aligned and moving.
- Build scalable, repeatable processes that cut time-to-value for every new customer after this one.
- Run regular check-ins with clients to surface concerns and clear roadblocks before they become escalations.
- Monitor and report on implementation metrics and KPIs, and facilitate clean handoffs from implementation to ongoing customer success.
- You get real satisfaction from the moment a hard integration finally runs clean and a customer sees their own data answer a question they couldn't answer before.
- You want to build the implementation function, not just work inside one — and you'd rather have the pen than the ticket queue.
Будет плюсом
- Experience with marketing and advertising data — CRM, marketing automation, ad platforms, ad servers, DSPs, web and app analytics, or media spend files. Knowing why a media taxonomy is never clean is a genuine advantage here.
- Background at a martech, measurement, or analytics vendor, or inside a large advertiser's or agency's data function.
- Familiarity with enterprise procurement, security review, and data governance processes.
- Startup or high-growth experience where you built the process rather than inherited it.
- Experience mentoring or setting standards for other implementation staff.
Другое
- Location: Chicago, IL — Hybrid, with regular onsite time at customer sites in the Chicago area
- 5–8+ years in enterprise implementation, technical account management, solutions consulting, or technical customer success at a B2B SaaS, data, cloud, or AI company.
- A track record owning enterprise onboarding end to end — from kickoff through go-live — with named references you're proud of.
- Real comfort with data integration mechanics: APIs, ETL/ELT pipelines, cloud warehouses (Snowflake, BigQuery, Redshift), file-based transfer, and troubleshooting a broken feed without escalating on day one.
- SQL fluency; Python or scripting is a plus.
- Project management discipline — you can hold a multi-workstream plan and a skeptical executive stakeholder at the same time.
- Clear written and verbal communication with both engineers and C-level stakeholders.
- Implementation methodology: Develop and refine the methodology that drives faster time-to-value across all of our enterprise clients.
- Cross-functional leadership: Become a key connector between customer success and product development, with real influence on what gets built.
- Process optimization: Design the approaches that streamline implementation and raise customer satisfaction — and own them as we scale.
- You own whether it works: Implementation isn't downstream of the value here — it is the value. Causal results are only as trustworthy as the data foundation you build.
- Elite customers, real stakes: You'll onboard Fortune 100 companies making multimillion-dollar marketing decisions, and work directly with the executives who make them.
- Build the function: You'll define the implementation methodology at a Series B company that's still writing its playbook, and see your process applied to every customer after yours.
- A product worth deploying: Causal AI running on our own NVIDIA DGX SuperPOD, doing something no attribution model has done before — customers are genuinely excited when it goes live.
- Series B momentum, real ownership: Meaningful equity at a Series B company that's raised $145M, with proven product-market fit and enterprise traction.
- This is implementation, not account management. If you want a renewal number, a QBR calendar, and an expansion quota, this isn't that role.
- You want a mature, documented onboarding process to execute against. Ours is partly built, and you'll be finishing it while running live projects.
- You'd rather hand data problems to engineering than get into the pipeline yourself. Here you'll be close to the data, often before it's clean.
- You want a fully remote role. The in-person time with customers is deliberate.