• Lead the design and execution of rigorous policy analysis projects and support project leadership across work of increased scope and complexity. • Provide high-level expertise on evidence-based strategies related to accountability (school designations, measures, state ESSA plans, waivers, local supports). • Assume primary responsibility for writing reports and presentations that describe and interpret findings for clients, partners, and stakeholders, with a focus on accountability and school improvement. • Design and apply appropriate qualitative and/or quantitative methods to collect, manage, analyze, and interpret data, maintaining AIR’s quality standards. • Translate complex findings into clear, actionable insights and recommendations for client reports, technical memos, and presentations. • Manage projects or major workstreams of increased scope and complexity, including planning timelines, overseeing deliverables, and managing project-level client relationships. • Engage with stakeholders and partners through meetings, briefings, and dissemination activities. • Contribute to a collaborative, inclusive work environment by providing guidance and feedback to team members and supporting high-quality project outcomes.

• Drive the research and development of next-generation deep learning models for protein-ligand co-folding and affinity prediction. • Bridge from research to commercial utility by equipping SandboxAQ’s software products with advanced predictive capabilities. • Bring novel ideas and the content of scientific papers into the ideation, training, and benchmarking of complex models, ensuring they are optimized for large-scale, real-world drug discovery applications. • Act as a technical beacon for the team, representing SandboxAQ scientifically and shaping its vision externally and internally. • Mentor junior researchers and collaborate across engineering and product teams to foster a culture of technical rigor and rapid iteration.

• Collaborate with colleagues on production systems and applications • Design, experiment, and evaluate algorithms as well as models using PyTorch, scikit-learn, Tensorflow, HuggingFace • Work on POCs and research prototypes • Provide thought leadership in machine learning • Represent Moody’s Analytics at scientific and industry conferences • Lead collaboration with colleagues and academia to publish research findings in leading academic venues such as ACL, EMNLP, NAACL-HLT, AAAI, KDD, CIKM, SIGIR, ECIR

Role Description The Research Assistant will support a project that monitors electronic cigarette (e-cigarette) use among youth and young adults in the United States (US). The multi-state initiative aims to complement existing surveillance activities that monitor tobacco product use with innovative rapid response data collection strategies. - Obtain real-time data for select states to evaluate the impact of flavored tobacco policies on e-cigarette sales volume, market share, and product or brand consumption. - Implement a national longitudinal survey of youth and young adults. - Support the regular categorization and validation of a broad range of tobacco products by flavor and sub-product type across various data sources. - Conduct literature reviews to address identified research gaps and support project activities. - Coordinate with contracted coders to maintain analysis timelines, data quality, and documentation. This position is eligible for 100% remote work for US-based candidates; however, candidates in the Atlanta, Georgia, Boston, Massachusetts, or Houston, Texas areas are preferred. Relocation expenses are not included. Qualifications - Bachelor’s degree in public health, statistics, epidemiology, biostatistics, public policy, economics, or related field required. - At least one year of related work experience, including paid internships or part-time positions within public health, health administration, or social sciences. - Prior experience with or interest in tobacco control research, retail sales data, and/or artificial intelligence initiatives strongly preferred. - Self-starter with ability to work independently and efficiently while consistently delivering high-quality work under time constraints. - Strong attention to detail and scientific inquisitiveness, as demonstrated by prior experience supporting fundamental components of research projects or tasks. - Strong written and oral communication skills, including the preparation and presentation of reports. - Strong technical skills, including word processing and spreadsheet management. - Strong organizational, time management, interpersonal, and collaboration skills. Requirements - Assist with categorizing features of tobacco and nicotine products (e.g., e-cigarettes, cigarettes) such as categorizing flavors and subtypes of products, to prepare for analysis. - Create and maintain technical documentation for standard procedures for coding and analysis. - Support the maintenance of a database of tobacco and nicotine product features (including product type, subtype, and flavor) by Universal Product Code (UPC). - Calculate percent change and other descriptive statistics to be used in data reports and presentations. - Compare categorizations of multiple contract data coders to identify and resolve discrepancies, using logical comparison operators in Microsoft Excel. - Provide iterative feedback to contract data coders to ensure consistent and high-quality work outputs. - Conduct investigative research activities, such as literature reviews or internet explorations, to ensure tobacco product feature categorizations align with evolving public health needs. - Support development of comprehensive reports that include findings from the analysis and evaluation of project activities. - Other duties and project responsibilities as assigned. - Travel up to 10 percent domestically. Benefits - Location: Remote, within the United States. Relocation expenses are not provided. - Salary Range: $55,000-$65,000, based on education, experience, and qualifications. - Position Type: Grant funded, limited-term opportunity contingent upon funding. - Position End Date: December 31, 2026.