About the TeamWhen a Fortune 500 hands its customer experience to an AI agent, it is one of the largest bets the business will make that year - and enterprises of that size do not make it on a demo. Each customer has their own requirements for how an AI agent should be evaluated, and the narratives we build have to start there and hold up long after the decision is made. Data plays a central role in almost every one of those narratives, which is why Agent Data Science exists.
A deployment is evaluated by many stakeholders inside the customer's organization, including senior leaders, customer experience teams, and technical functions, each with their own criteria for success. You own how performance is defined, measured, and demonstrated against these criteria, from translating them into metrics we operationalize and deliver, to improving the ones that are key to each evaluation. You hold that from the first pre-sales evaluation through the life of the account.
Our team sits within Agent Product Management and focuses mostly on our largest and most complex deployments, while supporting the data needs of Decagon customers more broadly. We are small relative to that surface area, which means the scope you own is very wide and exceptionally cross-functional. The work compounds accordingly: every metric you define on a flagship deployment becomes the default for the deals that follow, and the frameworks you build outlive any single account.
Demonstrating the value of our product is everyone's responsibility, not only ours. So the primary leverage we create comes from making the rest of the organization better at it. That is what lets us stay focused on the hardest and most novel measurement problems our customers bring us, which gives this team real influence over the long-term product roadmap.
About the RoleMost of your time will be spent working directly with customers to establish what should be measured and then building what produces it. Usually, the hard part comes from driving alignment, not the analysis itself.
You own both reporting on performance and improving it, working alongside the Agent Product Managers, Agent Strategy Managers, Strategic Account Directors, and Agent Deployment Engineers on each account. Beyond the work you do with individual customers, you will build the product features and frameworks that make everyone else at Decagon better at this work.
This is a rare combination: the rigor of a strong quant, the presence of a trusted executive advisor, and the ownership of a founder. If you have ever wanted to own the metric rather than report on it, this role was designed for you.
This is a senior individual contributor role. You are trusted to make high-stakes decisions independently and own outcomes.
In this role, you will- Work directly with customers to define the metrics that will be used to measure performance, then operationalize them so that both sides use and trust them.
- Own the delivery of those metrics, where transparent access to data helps reinforce the narrative we build with the customer.
- Own improving the metrics that matter most on your accounts, from diagnosing what drives performance to designing the experiments and driving the changes that move them.
- Scope and build the product features, tools, and frameworks that empower the rest of Decagon to do more of this work themselves.
- Define what success looks like for new initiatives that have no precedent. As Decagon's product continues to evolve, new surface areas will emerge where the right way to measure success has to be invented rather than applied.
- Run tight feedback loops into Product, Engineering, and Research, shaping the product based on what you learn measuring our most demanding deployments.
Your background looks something like this- 5+ years in data science, analytics, or another quantitative role where you drove analyses end to end and used them to move decisions, whether at a high-growth technology company, in consulting, or in a forward-deployed or solutions role.
- Exceptional judgment when interpreting metrics. You think through the edge cases quickly, and you can see how the same number will be read by someone arguing for us and against us. This includes 80/20 intuition on what's most important vs. analyzing everything.
- Comfort in fast-moving, ambiguous situations where you shape the approach as much as you implement it. Given a question with no established right answer, you can identify the best available option and defend it.
- Strong independence. You operate with very little day-to-day direction, set your own priorities against a broad mandate, and bring decisions rather than questions. You are comfortable being the person who defines the work rather than the person who receives it.
- Ability to bring stakeholders to a shared definition of success, even when they arrive with different priorities and different levels of technical depth.
- Strong technical fundamentals. Fluency in SQL and Python, and comfort working with large volumes of messy, unstructured conversational data.
Even better if you have- A history of building internal tooling that gained meaningful adoption, and the judgment to recognize when a problem should stay a one-off.
- Experience working on AI products in production, including evaluating LLM or agent behavior and building the evaluations yourself.
Compensation$165K-$215K + Equity
BenefitsWe proudly offer the following benefits for our full-time employees:
- Medical, Dental, and Vision benefits for you and your family
- Life Insurance and Disability Benefits
- Retirement Plan (e.g., 401K, pension)
- Parental Leave
- Fertility and family building benefits through Carrot
- Monthly stipend to support your wellness, lifestyle, and work-life balance
- Daily lunches and snacks in the office to keep you at your best
- Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)
These benefits are described in more detail in Decagon's policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.
