Middle Applied Scientist
Core
Improve how customers discover, evaluate, and select content for AI training datasets by owning scientific projects from problem framing through recommendation.
Role type
Middle Applied Scientist (Data Curation & Evaluation)
Builds
Improvements to the Data Licensing platform for a two-sided marketplace of high-quality media content.
Domain
AI/ML systems for content discovery and dataset curation
Deliverable
production ML models
Required skills
statistics, experimental design, quantitative problem-solving, search/recommendation/ranking evaluation, Python, SQL, sampling, clustering, classification, optimisation, uncertainty estimation, Bayesian methods, structured error analysis
Preferred skills
embedding models, semantic search, information retrieval, multimodal content, LLM-based workflows, agentic workflows, annotation workflows, full-population quality estimation
Technologies
Python, SQL, AWS, GCP
Responsibilities
Partner with senior data scientists to improve dataset discovery, qualification, and selection; Develop and apply evaluation and sampling methods to measure dataset relevance, coverage, diversity, and representativeness; Evaluate automated dataset-curation workflows using structured error analysis; Design and analyse experiments using offline evaluation and product telemetry.
Seniority
Middle, hands-on IC
