Role Description We are looking for a construction workforce researcher, academic leader, labor economist, or industry expert to join AlphaHire’s Academic & Research Advisory Council focused on construction labor intelligence, workforce forecasting, and operational market analysis. This is a fully remote opportunity, with optional onsite visits to client headquarters as desired. AlphaHire is building a construction workforce intelligence platform designed to help contractors, developers, and construction executives better understand labor scarcity, compensation acceleration, hiring pressure, and operational risk across the construction industry. The Academic & Research Council serves as a strategic layer that helps guide methodologies, validate workforce interpretation frameworks, improve dashboard structures, and contribute to industry research initiatives. This advisory seat is intended for professionals who want to influence how labor market intelligence is measured, interpreted, and communicated within the built environment sector. - Provide strategic guidance on workforce intelligence methodologies, labor market interpretation frameworks, and construction workforce measurement systems - Contribute feedback on labor dashboards, compensation indexes, hiring pressure models, and workforce forecasting structures - Support the development of research papers, whitepapers, executive briefings, and workforce intelligence publications - Participate in periodic advisory discussions focused on labor scarcity, operational workforce pressure, and construction market trends - Help validate the operational interpretation of workforce signals and regional labor market data - Advise on statistical modeling, benchmarking frameworks, and longitudinal workforce analysis initiatives - Assist AlphaHire in improving the academic rigor and credibility of its labor intelligence ecosystem - Collaborate with executive advisors and industry operators to bridge workforce research with real-world construction execution challenges - Provide thought leadership around workforce development, labor economics, operational planning, and construction industry transformation Qualifications - 10+ years of experience in construction management education, labor economics, workforce research, operations research, or related fields - Experience conducting workforce studies, labor analysis, market research, or operational intelligence initiatives - Strong understanding of construction labor markets, workforce planning, compensation dynamics, or operational workforce challenges - Experience with data interpretation, research methodologies, forecasting models, or workforce analytics preferred - Prior publication, speaking, advisory, or institutional research experience strongly preferred - Ability to contribute strategic insight rather than purely theoretical commentary - Experience collaborating with industry stakeholders, executive teams, or operational leadership groups preferred - Construction-industry exposure is strongly preferred; unrelated academic backgrounds may not align with the council’s objectives Requirements - Compensation: Advisory participation structure will vary based on experience, involvement level, and strategic contribution. Benefits - Participation in executive workforce intelligence initiatives - Collaboration on industry research and publications - Speaking and thought leadership opportunities - Direct engagement with construction industry operators and leadership teams - Access to select retained search assignments - Strategic advisory opportunities - Executive introductions - Participation in workforce intelligence discussions tied to active construction market expansion and hiring initiatives

Role Description Brandeis University’s Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 130 Business Intelligence and Visual 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 focus on storytelling with data, visual perception, and AI-enhanced dashboarding. Students create executive-ready reports using BI platforms. 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 (Master's or Ph.D) in Analytics, Strategic Analytics, Strategic Leadership, Information Design, Data Science, Software Engineering, or related field. - Experience using BI and visual analytics tools in industry or consulting contexts. - Experience in business intelligence, data visualization, and analytics storytelling, with the ability to translate insights into strategic actions. - 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). Benefits - Compensation for this position is: $6573.15. Application Process - 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. 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").

• Build & maintain marketing performance dashboards and reports • Automate recurring reporting & operational workflows to reduce manual effort and improve accuracy • Support the management and QA for marketing data pipelines across paid channels • Monitor day-to-day performance across acquisition channels, surfacing insights to marketing team stakeholders • Support channel leaders in implementing incrementality tests and strategies • Conduct deep-dive analyses to support media buying decisions with data-driven insights and recommendations

• Design, develop, and maintain interactive Tableau dashboards and reports. • Transform complex datasets into clear and meaningful visualizations that support business decision-making. • Ensure data accuracy, consistency, and integrity across all reporting solutions. • Analyze large datasets to identify trends, patterns, and business insights. • Deliver actionable recommendations based on data findings. • Collaborate with business stakeholders to gather reporting requirements and deliver scalable solutions. • Clearly communicate technical findings to both technical and non-technical audiences. • Optimize Tableau dashboards and reports for performance and scalability.