About Churney
At Churney, we’re building the signal layer between company data and AI and advertising systems, enabling business-outcome-optimized AI at scale. We help advertisers make smarter, long-term decisions. We've built a system that maximizes Return on Ad Spend (ROAS) by sending predicted lifetime value (pLTV) conversion events to platforms like Google and Meta. The result: better bidding, higher returns and measurable impact.
We’re a small team growing fast, in a research-driven environment with ambitious, highly capable and collaborative teammates. Our ambition is to build the best experimentation culture in the world, and we value shared learning above all else. The impact of your work is directly measurable so you'll have a uniquely fast feedback loop on the outcomes you’re driving.
You’ll be expected to bring high energy, a growth mindset and a deep care for clients.
- You'll be trusted early. You won't spend weeks shadowing. You'll own real client relationships quickly, and what matters is whether those clients grow with Churney.
- We expect you to be hungry to learn the ins and outs of our tech stack, the ad platforms, and how pLTV can improve advertisers across different industries.
- This is not a 9-to-5. We are growing fast and reward people who own outcomes and don't stop at "good enough".
The role
You’ll be the technical bridge between Churney and our clients - integrating their data, shaping modeling decisions, running our ML pipelines to turn raw user data into value signals, validating real-world impact in the ad platforms, ensuring a smooth onboarding experience, and guiding clients to adopt our services at scale.
It's a genuinely hybrid role spanning data engineering, ML, ad-platform measurement, and client communication — which means broad exposure to the whole business and the chance to become a domain expert in an industry that turns over hundreds of billions of dollars a year, while working alongside some of the best engineers around.
We are looking for smart, motivated problem solvers who learn fast and want their work to have measurable impact. We welcome both junior and experienced candidates.
What you'll do
- Integrate client data — connect cloud data warehouses (BigQuery, Snowflake, Redshift) and map messy, real-world data into our common data model.
- Configure and run our pLTV pipelines — set up and experiment within our production modeling pipeline to produce pLTV value signals.
- Own measurement and experimentation — design and run experiments (geo, A/B, lift, pre/post), diagnose signal loss and match quality in the Meta and Google interfaces (EMQ, signal ratio), and size the opportunity before launch.
- Validate impact and iterate — analyze results, confirm the signal is driving real ROAS, and tighten the loop.
- Communicate with clients — build a collaborative partnership with the client, present opportunity sizing and results to client stakeholders, proactively resolve issues, and drive adoption at scale.
- Improve the platform — collaborate with client leads and internal teams to automate toil and raise the quality of our tools and processes.
What we're looking for
- Strong Python and SQL — comfortable in large, messy datasets.
- Solid ML foundations — you understand modeling trade-offs even if you're not training models from scratch here.
- Excellent communication — you can explain a technical result to a non-technical client and hold the relationship.
- A measurement mindset (or strong appetite for one) — curiosity about how ad attribution, identifiers, and incrementality actually work.
- Ownership and comfort with ambiguity — you can run multiple client projects at once in a space where the playbook is still being written.
- An automation and improvement instinct — you'd rather fix the workflow than repeat the toil.
Nice to have
- Experience with ad platforms and performance marketing (Google, Meta, TikTok).
- Direct, hands-on client experience.
- Modern cloud data warehouses (BigQuery, Snowflake, Redshift).
- Background in A/B testing, causal inference, or LTV modeling.
- Experience building internal tooling or agentic AI workflows to speed up the above.
If you learn quickly, thrive in a fast-paced environment, and want to apply your data science skills to real, measurable client impact, we'd love to talk.
