SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
About the role
The Data team at SpaceXSI is a small team that works across the company to inform business and product decisions, and we’re hiring the first member of our team to focus fully on marketing. Performance Marketing at SpaceXSI drives scaled acquisition across 10+ channels for Grok, Grok Bot, and our full portfolio of AI products, across consumer, SMB, startup, and enterprise audiences.
This role will sit in the central Data team and partner directly with Marketing leadership to own the measurement and analysis of performance marketing, growth marketing programs, and overall marketing measurement. You'll use your expertise to inform where the next dollar goes as we scale paid acquisition, while building the foundations of a world class marketing data team. Your job is to help us understand the impact of our activities and what we should do next.
What you'll do
Build and own the measurement layer for performance marketing: economics and performance by channel, campaign, audience, geo, and creative, across both PLG and Sales-Led revenue lines
Design incrementality tests (geo, audience, and conversion lift) with channel owners, and use the results to move budget and calibrate attribution, not just to fill a slide
Stand up and maintain our media-mix model, and pair it with test results to answer the big allocation questions: which channels to scale, which to cut, and where we're hitting saturation
Own the quality of the predictive LTV signal we send to Meta, Google, and the other networks: validate it against realized revenue, catch drift and breakage early, and partner with Data and Engineering to improve it
Keep pipeline source and campaign attribution separate and clean, so self-reported source, promo codes, click-based attribution, and CRM pipeline each answer their own question without double counting revenue
You may be a fit if
You have 7+ years of experience in marketing analytics, marketing science, or data science supporting paid acquisition
You have strong SQL and python skills and are comfortable building both data pipelines and predictive models
You've run hold-out or incrementality tests and worked with MMM and attribution models in production, and you know the limitations of each
You understand how ad platforms report, optimize, and ingest conversion signals, and you don't take platform-reported numbers at face value
You communicate clearly with channel managers, Finance, and leadership, and you're honest about what the data does and doesn't support
You're self-directed, comfortable owning ambiguous problems end to end
Skills Required
- 7+ years of experience in marketing analytics, marketing science, or data science supporting paid acquisition
- Strong SQL skills
- Strong Python skills
- Experience building data pipelines and predictive models
- Experience running hold-out or incrementality tests
- Production experience with media-mix modeling and attribution models
- Understanding of how advertising platforms report, optimize, and ingest conversion signals
- Clear communication with channel managers, Finance, and leadership
- Ability to work independently and own ambiguous problems end to end
What We Do
We'd like to automate coding. To advance that mission, we're building Cursor. Our work includes training the world’s most widely used coding models, creating infrastructure that supports billions of requests per day, and building better ways for humans and AIs to work together.
