Role Description Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey. Position: Data Science Experts Type: Contract Compensation: $70–$100/hour Location: Remote Commitment: 40 hours/week Role Responsibilities - Guide research and engineering teams on data science methodology, statistical inference, and modeling best practices. - Design challenging data science tasks and write accurate, well-structured analytical solutions. - Evaluate data science tasks and solutions produced by AI systems and other experts; provide clear written technical feedback to improve correctness and rigor. - Develop guidelines and evaluation frameworks to assess the quality of statistical reasoning, ML pipeline design, and data analysis approaches. - Collaborate with other subject matter experts to ensure consistency and accuracy in training data. Qualifications - Must-Have: 3+ years of professional or research experience in data science, data analysis, statistical modeling, or applied machine learning. - Ability to commit to 40 hours per week during weekdays for the duration of the engagement. - Strong written communication skills and the ability to explain analytical decisions and modeling choices clearly. - Preferred: Prior experience with data annotation, labeling, evaluation, or human feedback collection. - Experience with LLMs, AI systems, or agentic workflows; familiarity with agentic frameworks. Requirements - W-2 employment with Cincinnatus LLC. Application Process - Upload resume - AI interview based on your resume - Submit form Resources & Support - For details about the interview process and platform information, please check: Interview Process - For any help or support, reach out to: support@mercor.com PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

• Own end-to-end portfolio risk analytics for Flex's credit card book • Analyze approval thresholds, bureau cutoff performance, and risk-tiered line sizing • Incorporate existing Flex relationship data into credit decisioning and line management frameworks • Monitor authorization patterns and spend velocity for credit quality • Track minimum payment rates and utilization trends • Maintain vintage curves and delinquency migration analysis • Analyze loss emergence patterns by segment and cohort • Build and maintain early warning frameworks for credit deterioration • Synthesize data from multiple sources for portfolio health • Lead periodic portfolio reviews and present findings to senior leadership • Develop credit risk segmentation for precise limit management • Partner cross-functionally with various teams to ensure risk visibility • Contribute to stress testing and scenario analysis

• Contribute to multiple geospatial AI and Earth observation initiatives. • Focus on operationalisation of GeoAI services using AI and large-scale Earth observation processing. • Support crop type mapping projects and scale algorithms for digital agriculture. • Develop and improve ML/CV algorithms for large-scale remote sensing data processing on Sentinel data. • Analyse data flows and optimise databases and queries. • Collaborate with technical and non-technical stakeholders in an international setting.

• Design, build, and deploy AI-enabled services for the Compendia Data Platform (CDP), including LLM-based NLP capabilities and interoperable (agentic) components. • Develop and maintain evaluation frameworks for LLM - ontology/knowledge base integration, including quantitative metrics, baselines, error analysis, and reporting to support defensible architecture decisions. • Support ontology lifecycle management (modeling, inference, validation, and lifecycle workflows) as the ontology module matures from MVP to GA. • Partner with product and engineering to improve platform stability, performance, and scalability for CDP v1.8 and beyond. • Contribute to proofs of concept (PoCs), demos, and mission-aligned analytics workflows while sustaining LANL delivery commitments. • Stay current on relevant research and emerging patterns for semantic engineering, LLM evaluation, and production AI systems; translate findings into implementable, testable solutions. • Contribute to a high-performing team culture by delivering mission-aligned analytics and software tools that ensure high customer satisfaction and meet strict delivery commitments.