Data Scientist - Growth

What you will be doing

• Own the Growth data model: turn raw data from our CRM, marketing, messaging, sales engagement and support systems into trusted, documented dbt models that join cleanly to product usage.

• Bring new GTM sources into the warehouse when you need them, and partner with data engineering on anything that needs to scale beyond your immediate use case.

• Work with RevOps and GTM leaders on the metrics that matter (pipeline, conversion, attribution, CAC, retention and expansion), so each has one trusted definition across teams.

• Together with RevOps and Marketing Ops, get modelled data back into the business systems, so Sales, Marketing and Customer Success can act on it in the tools they already use.

• Flag the field and process gaps that make CRM data hard to trust, and help RevOps and Marketing Ops fix them at the source.

• Run the deep dives that move the business: where pipeline leaks, which campaigns create revenue, what wins a pilot, which accounts are at risk or ready to grow. Proactively surface the questions nobody is asking yet.

• Build dashboards and reporting that scale beyond you, so GTM teams can answer their own questions where possible.

• Help shape how the data team operates as we scale: standards, tooling, ways of working.

What you'll need

• Strong proficiency in SQL and Python.

• Genuine depth in data modelling and analytics engineering: dbt, dimensional modelling, testing, semantic layers, and the judgement to build models others trust and reuse.

• Hands-on experience modelling data from growth-side business systems, for example:

  • CRM (Salesforce, HubSpot)

  • Marketing automation and messaging (Customer.io, Braze, Marketo, Iterable)

  • Sales engagement and conversation intelligence (Outreach, Salesloft, Gong)

  • Support and in-app messaging (Intercom, Zendesk)

• You know these systems' quirks: overwritten fields, missing history, duplicate contacts, and opportunity stages that mean different things to different teams.

• A solid grasp of GTM analytics: funnels, attribution, pipeline and forecasting, cohort retention, and net revenue retention.

• Excellent communication skills and the confidence to influence decisions through data storytelling, including pushing back when the data doesn't support what someone wants to do.

Nice to have

• Experience in B2B SaaS with an enterprise, sales-led motion.

• Hands-on experience with our stack: Snowflake, dbt, Hex, Dagster, dlt, Salesforce.

• Experience with reverse ETL or data activation, whether a tool like Hightouch or Census or syncs you built yourself.

• Experimentation or modelling on GTM questions: campaign holdouts, uplift, propensity or forecasting models.

• Prior experience as an early data hire at a fast-growing company, i.e. you've built the muscle, not inherited it.