人工智能数据负责人
AI Data Lead
Data-AI 主导
关于我们
OurRitual 是一个快速发展的数字平台,正在重新定义关系疗法的方式。我们结合专家主导的治疗课程与人工智能支持,创造一种融合体验——临床深度与智能技术交织在一起,形成单靠任何一方都无法实现的效果。我们称之为持续治疗。我们已实现产品-市场匹配,实现了强劲增长、卓越的参与度和留存率,打破了该领域的常规。我们处于 A 轮前阶段,此时人工智能从功能转变为基础——而这一转变贯穿整个数据领域。
职位概述
作为数据主导,你将负责公司内如何将数据转化为决策,并领导从传统 BI 向 AI 驱动的 BI 的转变,由代理实时呈现洞察。你将与一名数据工程师紧密合作,随着范围扩大,有空间扩展团队。
你将定义当 AI 嵌入数据功能时的样子,而不是作为附加功能——从底层基础设施到人们实际使用的仪表板和代理。AI 原生 BI 的操作手册仍在编写中,而你将撰写其中的大部分内容。
主要职责
数据战略与所有权
- 负责数据功能的愿景和路线图,包括从传统 BI 向基于代理的 AI 驱动 BI 的转变
- 与高级管理层合作设定优先级
- 今天与一名数据工程师密切合作,随着功能扩展,可选择扩展团队
AI 驱动的 BI 与代理
- 推动 AI 在数据工作流中的采用——从自动化分析到代理实时回答业务问题
- 负责语义和指标层,确保每个仪表板和每个代理都使用相同的定义
- 建立基于 git 的工作流程:仪表板和查询存储在 git 中,通过 PR 进行编辑,由代理构建
- 通过 MCP 将我们的数据暴露给代理,使业务问题得到解答而无需等待仪表板
- 定义 AI 提升效率的地方——以及人类判断仍需保持底线的地方
基础设施与分析
- 负责数据基础设施:BigQuery、dbt、管道和数据可靠性
- 主导复杂的分析工作、A/B 测试和影响评估,亲自参与
- 定义 AI 功能和代理的评估和质量指标——不仅仅是仪表板上的 A/B 测试
跨职能协作
- 与产品、营销、财务、运营和工程部门合作,定义分析需求并将其转化为解决方案
- 代表数据功能向组织其他部分传达
查看英文原文
Data-AI Lead
About Us
OurRitual is a fast-growing digital platform reshaping how relationship therapy works. We combine expert-led therapy sessions with AI-powered support to create a blended experience — where clinical depth and intelligent technology weave together into something neither could deliver alone. We call it Continuous Therapy. We've reached product-market fit with strong growth, exceptional engagement, and retention that breaks the category. We're pre-Series A, at the stage where AI moves from feature to foundation — and that shift runs straight through data.
Role Overview
As Data Lead, you will own how data gets turned into decisions across the company — and lead the shift from traditional BI into AI-driven BI, powered by agents that surface insight in real time. You'll work closely with one data engineer today, with room to grow the team as scope expands.
You'll define what the data function looks like when AI is embedded in it, not bolted on — from the underlying infrastructure to the dashboards and agents people actually use. The playbook for AI-native BI is still being written, and you'll write a lot of it.
Key Responsibilities
Data Strategy & Ownership
- Own the vision and roadmap for the data function, including the shift from classic BI to agent-based, AI-driven BI
- Set priorities in partnership with senior leadership
- Work closely with a data engineer today, with the option to grow the team as the function scales
AI-Driven BI & Agents
- Drive adoption of AI across data workflows — from automating analyses to agents answering business questions in real time
- Own the semantic and metrics layer so every dashboard and every agent reads the same definitions
- Build in a git-based workflow: dashboards and queries live in git, edited through PRs, built with agents
- Expose our data to agents over MCP so business questions get answered without waiting for a dashboard
- Define where AI speeds things up — and where human judgment still has to hold the line
Infrastructure & Analytics
- Own the data infrastructure: BigQuery, dbt, pipelines, and data reliability
- Lead complex analytics work, A/B testing, and impact measurement, hands-on
- Define evals and quality metrics for AI features and agents — not just A/B tests on dashboards
Cross-functional Collaboration
- Partner with product, marketing, finance, operations, and engineering to define analytical needs and turn them into solutions
- Represent the data function to the organization — senior-level storytelling with data
Skills & Qualifications
- 7+ years of experience in data roles, spanning both analytics and infrastructure
- Strong analytical foundation — you understand what makes analysis rigorous, not just what makes it automatable, and can use that to build an AI-driven analytics system that's actually trustworthy
- Hands-on experience integrating AI into data workflows — building it, not just using it; you've shipped an agent or AI analysis flow that people actually use
- Strong SQL skills and experience building scalable data infrastructure
- Track record owning data projects independently with stakeholders across the business
- Solid grounding in experimentation and statistical methods
- Excellent communication and presentation skills at a leadership level
- Comfortable being the most senior (and often only) data voice in the room
Nice-to-Have Skills
- Experience with dbt
- Experience with Amplitude
- Experience with BigQuery
- Experience with git-based BI workflows or tools like Modus
- B2C environment experience
Why This Role
Data teams are about to look very different — not because dashboards disappear, but because people expect answers faster than a weekly report can deliver. This is a rare opportunity to define what AI-native data leadership looks like at a company sitting on a dataset that doesn't exist anywhere else, with real ownership over how the function grows. If you want to build the data function that a category-defining product deserves, this is it.
Originally posted on Himalayas