About the job
We are looking for experienced trust and safety data scientists to help us improve, productionise and monitor measurement for complex, actor- and sometimes network-level harms. A data scientist in this role will own measurement and metrics across several established harm verticals, including estimating prevalence for on-platform (and sometimes off-platform!) harm, and analyses to identify gaps and opportunities in our responses.
Responsibilities
Own measurement and quantitative analysis for a group of severe, actor- and network-based usage harm verticals.
Develop and implement AI-first methods for prevalence measurement and other productionised safety metrics, which may necessarily include off-platform indicators or other non-standard datasets.
Build metrics that can be used for goaling or A/B tests when prevalence or other top line metrics are not suitable.
Own dashboards and metrics reporting for harm verticals.
Conduct analyses and generate insights that inform improvements to review, detection, or enforcement, and that influence roadmaps.
Optimise LLM prompts for the purpose of measurement.
Collaborate w/ other safety teams to understand key safety concerns and create relevant policies that will support safety needs.
Provide metrics for leadership and external reporting.
Develop automation to scale yourself, leveraging our agentic products.
Qualifications
Minimum
Are a senior DS with trust and safety experience that can drive measurement direction.
Have deep statistics skills, specifically around sampling methods and prevalence estimation of complicated problem areas (ideally activity- rather than content-based).
Have experience working with severe and sensitive harm areas like child safety or violence.
Are an excellent communicator, and have strong cross-functional collaboration skills.
Are capable in data programming languages (R or python, SQL).
Preferred
(Ideally) have experience with AI harms or leveraging AI for measurement.