Own product instrumentation, event taxonomy, metrics, data pipelines, dashboards, experimentation, and predictive modeling. Build analytics for product and marketing decisions, then develop and deploy customer-facing LLM features and secure data-connected agents using retrieval, tool calling, and agent frameworks. Partner with engineering, marketing, product, and leadership to establish Quin’s data and AI foundations across IoT and safety products.
About Quin
What you'll do
Quin builds intelligent safety systems that combine hardware, software and real-time data to detect critical events and connect people to help when it matters most. Our products sit at the intersection of IoT, edge computing, and real-world human behaviour, generating rich datasets that power smarter decisions, better user experiences, and ultimately save lives.
The roleWe're hiring a Data Scientist to drive how Quin measures its products, and turn that data into AI our customers and team rely on every day.
Building on our existing data foundation, you’ll lead execution on analytics across our core products. That covers everything from Amplitude instrumentation to the metrics, dashboards, and predictive modelling that drive product and marketing decisions.
From there, you’ll help take data further with AI. You’ll bring intelligent features into our products and explore how agents can build on your work: surfacing insights faster, scaling analysis across products, and opening up new ways for the team to use data. You’ll decide where AI adds the most value, then design and ship it to run reliably in production.
Your work splits into three areas:
1. Instrumentation and metrics
- Own the tracking plan and event taxonomy across our core products.
- Implement Amplitude instrumentation client-side and server-side with engineering.
- Build pipelines from Amplitude and other sources into our warehouse, with data-quality checks.
- Define one documented metrics layer for activation, engagement, retention, conversion and LTV.
2. Analytics and dashboards
- Build real-time dashboards for product, marketing and leadership, each tied to a decision.
- Run the analysis behind product and marketing decisions: funnels, cohorts, retention and feature adoption.
- Collaborate with Marketing to design and read out A/B tests.
- Build predictive models for churn, segmentation, and conversion that feed product and lifecycle decisions.
3. AI on top of the metrics layer
- Bring AI features into our customer-facing products, working with engineering from prototype to production
- Build agents that run securely in isolated, containerised environments, with tightly scoped access to data and tools.
Analytics and data
- 4+ years in product analytics, data science or analytics engineering, ideally in a product-led company.
- You've owned product instrumentation end to end in Amplitude, Mixpanel or similar: tracking plans, event design, implementation and governance.
- Expert SQL and strong Python.
- Experience modelling data in a cloud warehouse (BigQuery, Snowflake, Redshift or Postgres)
- Strong experimentation and statistics: A/B test design, power analysis, funnel and cohort analysis.
- You've built dashboards that teams use to make decisions.
AI (hands-on experience required)
- You've built and shipped at least one LLM-powered feature, agent or automation that real users rely on.
- Hands-on with LLM APIs (Anthropic, OpenAI or similar), including tool use or function calling, and retrieval (RAG).
- Experience building agents that connect to real data and tools, using frameworks and protocols such as MCP, LangGraph, or the Claude Agent SDK.
- Hands-on experience with a semantic layer tool such as dbt Semantic Layer or Cube.
- Experience in wearables or IoT products
- Work on a mission-driven product with real world impact.
- Shape Quin's data and AI foundations from the ground up, with real ownership.
- Ship AI features that customers use in production.
- Work across product, engineering, marketing and leadership, not in a silo.
- Join a fast-moving team building at the edge of hardware and AI.
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