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: Economics & Finance Assessment Specialist Type: Contract Compensation: $40–$90/hour Location: Remote Commitment: 10+ hours/week Role Responsibilities - Author original economics and finance questions that test deep conceptual understanding, not surface-level recall. - Ensure questions are unambiguous, self-contained, and precisely defined. All necessary information must be in the problem statement. - Rate each question's difficulty: Medium (intro undergraduate), Hard (advanced undergraduate), or Expert (post-graduate and above). - Provide 1 correct answer and 9 plausible but subtly incorrect alternatives that challenge expert-level solvers. - Write step-by-step Chain-of-Thought solutions with clear, concise intermediate steps in markdown format. - Supply 1–5 academic references per question from reputable sources like peer-reviewed journals and university repositories. - For verification tasks: flag issues with clarity, completeness, precision, or solvability and justify any edits made. Qualifications - Must-Have: PhD or doctoral candidate in Economics, Finance, or a closely related field. - Master's degree considered for candidates with exceptional depth in a specific subdomain. - Strong command of graduate-level economic theory, quantitative methods, and financial modeling. - Excellent written English and ability to express complex ideas clearly and concisely. - Preferred: Experience with academic research, CFA/CPA certification, or financial industry expertise is a strong plus. 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.

Role Description Brandeis University’s Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 140 Generative AI and Large Language Models in Analytics for the Fall-2 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Applied Data Science and Decision Analytics. This course will examine the design, implementation, and governance of generative AI systems particularly Large Language Models (LLMs) for analytics and decision-support applications. Students explore: - Prompt engineering - Fine-tuning - Evaluation metrics - Hallucination mitigation - Responsible deployment practices Core Course Responsibilities Summary - Course Logistics and Facilitation: Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms. - Instructor Presence and Engagement: Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation. - Individual Feedback and Grading: Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe. - Professional Conduct and Standards: Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term. Qualifications - Required: - Advanced degree (Masters or Ph.D) in Computer Science, Data Science, or Software Engineering or AI Related fields. - Experience applying LLMs in Enterprise or Analytics contexts, including prompt engineering, model evaluation, hallucination mitigation, and responsible deployment of LLM-Based systems for analytics and or decision support. - At least 1 year of teaching or training experience (preferably online/asynchronous). - Experience with online instruction. - Excellent communication and teaching skills in an online learning environment. - Preferred: - Prior online teaching experience at the graduate level. - Knowledge of global learner personas and culturally responsive pedagogy. - Familiarity with Moodle LMS and digital authoring tools (e.g., H5P). Requirements - Interested candidates should submit: - A cover letter highlighting relevant qualifications and teaching experience. - A current CV or resume. - Contact information for three professional references. - Application review begins June 1, 2026 though we will continue to accept submissions on an ongoing basis. - This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509. Benefits - Compensation for this position is: $6573.15. - Pay Range Disclosure: The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements. Equal Opportunity Statement Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").

Role Description Brandeis University’s Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 135 Time Series Forecasting and Operational Analytics for the Fall-2 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Applied Data Science and Decision Analytics. This course will cover predictive modeling and forecasting under uncertainty, including ARIMA, Prophet, and deep learning approaches for sustainable operations. Core Course Responsibilities Summary - Course Logistics and Facilitation: Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms. - Instructor Presence and Engagement: Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation. - Individual Feedback and Grading: Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe. - Professional Conduct and Standards: Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term. Qualifications - Advanced degree (Masters or Ph.D) in Statistics, Operational Research, Data Science or a related field. - Professional experience applying forecasting methods to operational demands, planning, or in sustainability contexts. - Expertise in time series analysis and forecasting under uncertainty, including ARIMA and modern machine learning approaches. - At least 1 year of teaching or training experience (preferably online/asynchronous). - Experience with online instruction. - Excellent communication and teaching skills in an online learning environment. Requirements - Prior online teaching experience at the graduate level (preferred). - Knowledge of global learner personas and culturally responsive pedagogy (preferred). - Familiarity with Moodle LMS and digital authoring tools (e.g., H5P) (preferred). Application Process - A cover letter highlighting relevant qualifications and teaching experience. - A current CV or resume. - Contact information for three professional references. Application review begins June 1, 2026 though we will continue to accept submissions on an ongoing basis. Compensation Compensation for this position is: $6573.15 Pay Range Disclosure The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements. Equal Opportunity Statement Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").

• Develop and maintain interactive dashboards and data visualizations in Salesforce and/or Tableau to present program data. • Design and customize reports that meet business requirements using best practices and innovative approaches. • Pull, validate, and reconcile data from multiple systems and source files • Analyze actuals vs. forecast/prior period and highlight key variances and trends • Maintain reporting documentation, definitions, and process steps • Identify and implement opportunities to improve or automate manual reporting processes • Advocate for data visualization best practices, dashboard design and analytics storytelling. • Coordinate with program delivery teams to provide data for ad-hoc requests and to review client-facing program information for data accuracy. • Collaborate with software teams on dashboard development and integration of systems to provide maximum flexibility in data reporting and availability. • Work with software teams on database development and process improvements to optimize efficiency and streamline data processes. • Learn, operate, and integrate with company proprietary computer software and project management systems. • Ensure data quality, accuracy and integrity in all deliverables. • Manage project timelines, tracking billable hours as needed. • Other duties as assigned.