SME in Data Science for Liberal Arts

Job Title: SME in Data Science for Liberal Arts\n\nAbout the Opportunity:\nWe are seeking an experienced and academically strong Freelance Subject Matter Expert\n(SME) in Data Science for Liberal Arts to support the development of high-quality\nassessment content for an innovative educational publishing/learning initiative.\nThis role is ideal for professionals who are passionate about making data science\naccessible, relevant, and engaging for non-technical learners, particularly students from\nLiberal Arts and interdisciplinary backgrounds.\nThe selected SME will play a critical role in designing pedagogically sound, learner-centric\nassessments that evaluate conceptual understanding, interpretation skills, analytical\nthinking, and real-world application of data science principles — without requiring\nprogramming or coding exercises.\n\nKey Responsibilities:\nThe SME will be responsible for creating and reviewing a diverse range of assessment\ncontent aligned with chapter-wise learning objectives.\n\nAssessment Development:\nDesign and develop high-quality assessment items, including but not limited to:\n%CF; Multiple-Choice Questions (MCQs)\n%CF; Fill-in-the-Blank (FITB) questions\n%CF; Cloze / Drag-and-Drop activities\n%CF; Short-answer conceptual questions\n%CF; Data interpretation and analytical reasoning questions\n%CF; Scenario-based and case-study-driven problems\n\nKey Expectations:\nThe ideal candidate should demonstrate the ability to create assessments that are:\nConceptually Clear and Accurate: Ensure correctness of content, terminology, and explanations Present concepts in an accessible and learner-friendly manner\nPedagogically Strong: Maintain appropriate difficulty progression across chapters and question types. Align assessments closely with stated learning outcomes and instructional goals.\nEngaging and Application-Oriented: Develop questions that encourage critical thinking and real-world interpretation. Use contextualized examples relevant to Liberal Arts learners.\nPrecise and Unambiguous: Avoid vague wording or multiple interpretations Ensure all questions have clearly defensible answers and instructions.\n\nDesired Candidate Profile :\n%CF; Strong academic or industry background in Data Science, Statistics, Analytics, Computational Social Science, or related fields\n%CF; Experience teaching or developing curriculum for non-technical or interdisciplinary learners.\n\nPrior experience in:\nAssessment design\nEducational content development\nQuestion bank creation\nExcellent written English and attention to detail Ability to simplify technical concepts for diverse learner audiences\n\nPreferred Qualifications:\nCandidates with one or more of the following will be preferred:\nExperience working with educational publishers, EdTech organizations, or universities\nFamiliarity with Bloom’s Taxonomy and assessment frameworks Exposure to Liberal Arts, Social Sciences, Humanities, or interdisciplinary education models\nExperience designing assessments for online or digital learning environments\nSeniority Level