Trust and Safety - Senior Data Scientist

About the Role Scale is at the frontier of Generative AI and human-AI collaboration. The Gen AI Ops Trust and Safety team protects contributor integrity across a marketplace of hundreds of thousands of contributors training foundation models. We're looking for a modeling-focused Data Science Lead who ships fast, thinks in systems, and uses AI coding tools as a core part of their workflow. This is a high-autonomy IC role. You will own fraud and abuse detection models end-to-end — from label definition through feature engineering, training, evaluation, and production deployment. You'll work in a small team that operates at 10x velocity by pairing deep analytical judgment with AI-augmented development (Cursor, Claude Code). If you've felt limited by teams that move slowly or separate "analysis" from "building," this role eliminates that gap entirely. You Will: Own the model lifecycle — from label definition through feature engineering, training, evaluation, and production deployment. Ground truth is ambiguous and non-binary; you'll need to make it work anyway. Build and extend the feature store — design and productize signals across identity, behavioral, and third-party data sources that power detection models reliably at scale. Design and ship detection systems — not just notebooks. Rules, clustering, anomaly detection — all the way to production decisions. Move fast with AI tools — use AI-assisted IDE and copilots daily to prototype pipelines, run investigations, and automate workflows. This is how the team operates. Partner with Ops and Engineering to close the loop between model output and real-world actions, and continuously improve the contributor experience. Ideally You'd Have: 5–8 years in Data Science or Machine Learning, with at least one production fraud/abuse/integrity model shipped end to end (labels → features → deployment → iteration). Strong proficiency with AI coding assistants. You should already be using these tools daily and understand how to leverage them for rapid SQL/Python development, data exploration, and code generation — not as a novelty but as a core workflow. Deep feature engineering instinct — you see a messy behavioral log and immediately think about what signals it contains, how to extract them, and whether they'll hold up adversarially. Expert SQL and Python. Comfortable writing 200+ line CTEs, building data pipelines, and working with large-scale event data. Experience with unsupervised methods (clustering, anomaly detection) alongside supervised classification. Bonus if you've worked with semi-supervised approaches for noisy labels. Comfort operating across the full stack: Snowflake DDLs, Python modeling, rule engines, alerting systems, Google Docs/Sheets automation, workflow orchestration. Clear, direct communication. You can explain a model's precision/recall tradeoff to an ops lead and debug a Snowflake query in the same afternoon. Nice to Have: Marketplace or gig-economy platform experience. Experience building linkage/graph-based detection. Familiarity with identity verification vendors and their signal taxonomies. Experience building automated alerting and monitoring pipelines. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a l ... (truncated, view full listing at source)