Los Angeles, CA · On-site
$185K/yr
SENIOR DATA SCIENTIST COMPANY: STEVEN.COM REPORTING TO: HEAD OF DATA INTELLIGENCE LOCATION: LOS ANGELES SALARY: UP TO $185,000 ABOUT STEVEN.COM Steven.com is building the operating system for the ...
Los Angeles, CA · On-site
$185K/yr
SENIOR DATA SCIENTIST COMPANY: STEVEN.COM REPORTING TO: HEAD OF DATA INTELLIGENCE LOCATION: LOS ANGELES SALARY: UP TO $185,000 ABOUT STEVEN.COM Steven.com is building the operating system for the ...
$185K/yr
Full-time
Re-posted 13 hours ago
COMPANY: STEVEN.COM
REPORTING TO: HEAD OF DATA INTELLIGENCE
LOCATION: LOS ANGELES
SALARY: UP TO $185,000
Steven.com is building the operating system for the billion-dollar creator economy. Creators are held back by fragmented distribution, rented audiences, and technology that wasn't built for them. Steven.com is the unlock - the end-to-end Operating System designed to empower, grow, and scale what is irreplaceably human.
We work across four interconnected pillars: Creator Media (reach, influence, trust), Creator Community (turning audiences into connected tribes), Creator Ventures (infrastructure for creators to build and back aligned businesses), and Creator Technology & Data Intelligence - the proprietary data suite that fuels the entire flywheel.
Powering all of it is our Innovation and Technology Organisation (ITO) - Steven.com's founding technical engine, operating in stealth mode to build the data and AI infrastructure underpinning our next stage of growth. Data Intelligence sits at the core of our long-term competitive moat.
We're looking for a builder to sit at the intersection of proprietary data, applied machine learning, and creative/social intelligence - building models and systems that turn one of the most unique datasets in the creator economy into insight and competitive advantage. This is hands-on and high-ownership: you'll work closely with engineering and product to translate data into decision-making tools, intelligence products, and AI-powered capabilities.
Design, build, and deploy ML models and AI systems powering creator intelligence, audience analytics, and content performance products.
Take proprietary audience and creator models from feature engineering and training through to production deployment and monitoring.
Apply statistical rigour to extract actionable insight from large, complex, often unstructured datasets.
Work hands-on with LLMs and foundation models - fine-tuning, prompt engineering, RAG, and other post-training techniques.
Partner with business leaders to ensure statistical rigour underpins reporting and decision-support tools.
Contribute to the team's intellectual culture via technical blogs, internal research, and conference talks.
Building and shipping ML/statistical models in production - not just notebooks.
Strong Python fluency across the modern data science stack (PyTorch, TensorFlow, scikit-learn, or equivalent).
Operating at scale: large datasets, complex pipelines, and the engineering challenges that come with them.
Working with LLMs/foundation models and applying frontier ML research to real business problems.
You approach problems from first principles and interrogate whether a model is the right tool before reaching for one.
You're intellectually rigorous and honest - careful experiment design, appropriate scepticism, clear communication of uncertainty.
You think of data as a strategic asset and connect technical work to the business questions it answers.
You're energised by hard, ambiguous problems in messy, real-world environments - you don't need a clean brief to do great work.
You're a builder first: you get models into production, not just into a deck.
You're high ownership, low ego, and commercially minded.
You're intellectually curious - you read research, build outside of work, and bring fresh thinking to the team.
Demonstrable experience building and deploying ML or statistical models in production.
Strong Python and relevant DS library experience.
Background in consumer/enterprise data products, creator economy, or media analytics is a strong advantage.
Bonus: CS/ML research background, agentic AI or multi-model architecture experience, creator/audience/community data exposure, published research or open-source contributions, or time at organisations at the frontier of applied AI.