Role Description Lightly AG is looking for analytical and driven Geopolitical and Event Forecasting Professionals to help evaluate AI-generated predictions about real-world events. The estimated project scope is approximately 15 hours/week. - Evaluate AI-generated event forecasts by assessing the accuracy and quality of predictions covering elections, central bank decisions, policy announcements, and technology/regulatory events using a provided rubric. - Project expected to begin early July. Qualifications - At least 2-3 years of recent experience in political analysis, macroeconomics, geopolitical risk, or policy research. - Experience producing or evaluating probabilistic forecasts - election modelling, policy impact assessments, or macro scenario analysis. - Familiarity with prediction markets (Polymarket, Metaculus) or superforecasting methodologies. - Fluency in English (requirement). - Strong analytical and quantitative skills. - Ability to communicate clearly and precisely. Benefits - Work directly with a YC-backed team on cutting-edge AI evaluation projects. - Competitive compensation tailored to experience and scope of work. Company Description Lightly AG is an ETH and HSG spin-off pioneering in machine learning and computer vision. We are backed by Y Combinator and top-tier investors, and our technology is used by global leaders in autonomous driving, medical imaging, and visual inspection.

• Support research and studies related to Artificial Intelligence • Test and explore AI tools applied to software development and software quality • Assist in creating prompts, automated workflows and small prototypes • Support initiatives to automate tasks using AI • Participate in documenting processes, tests and learnings • Collaborate with Software Quality, Automation and Development teams • Contribute ideas for continuous improvement of processes and products • Participate in project meetings, training sessions and activities.

• Define and own the AI roadmap, starting with go-to-market and expanding across the business, from use-case discovery and prioritisation through to delivery, adoption and measurement. • Identify where AI, automation and workflow redesign can remove friction, in go-to-market first and then the wider business. • Act as the connective tissue between business stakeholders and technical teams, translating operational challenges into concrete, prioritised delivery work. • Build a clear intake, prioritisation and delivery model for AI use cases, ensuring the team focuses on high-impact problems rather than isolated experiments. • Evaluate build vs. buy decisions across AI tooling, workflow automation, business systems and internal productivity tools. • Champion practical, responsible AI adoption across the company, ensuring what gets built is useful, trusted and embedded into how people work. • Partner with Legal, Security, IT, Data Warehouse and Analytics teams to ensure AI adoption is scalable, compliant and aligned with internal governance. • Help teams move from AI experimentation to repeatable workflows that improve productivity, quality, accuracy and decision-making. • Lead and develop your Engineering Manager, who owns day-to-day technical execution for the technical delivery team. • Set technical direction and priorities alongside the EM by challenging scoping, reviewing tradeoffs, and removing blockers. • Provide enough technical fluency to challenge assumptions, evaluate solutions, and ensure the right problems are being solved in the right way. • Work closely with Data Warehouse and Analytics teams to ensure AI workflows are grounded in reliable data and aligned with existing data architecture. • Foster a culture of experimentation, pragmatic delivery, continuous improvement and measurable business impact. • Ensure AI and automation initiatives are delivered with appropriate documentation, ownership, adoption plans and feedback loops. • Own Revenue Operations: the systems and data behind the revenue engine (CRM, pipeline, lead routing and forecasting). • Make RevOps one of the first proving grounds for AI: improve accuracy, speed and rep productivity, and build it. • Drive operational change that sticks, ensuring teams do not just receive new tools but adopt better ways of working. • Build trust with senior stakeholders by clearly communicating priorities, trade-offs, risks and expected outcomes. • Create practical enablement, guidance and feedback mechanisms to support AI adoption across different functions. • Measure adoption and impact, using feedback and business outcomes to refine the AI Operations roadmap. • Help the business distinguish between genuine AI opportunities and low-value experimentation or hype.

• AI Use Case Discovery and Prioritization • Partner with clinical, operational, finance, human resources, and technology teams to identify repetitive, manual, or high-friction workflows that may benefit from AI-enabled automation. • Evaluate potential use cases based on business value, feasibility, scalability, and user impact. • Translate business needs into practical automation concepts, prototypes, and deployable solutions. • Identify opportunities to reduce administrative burden and improve employee productivity through responsible use of AI. • Solution Design, Build, and Deployment • Design, build, test, and deploy AI-powered workflows, chatbots, assistants, agents, and automations using approved enterprise tools and platforms. • Create prompts, instructions, knowledge libraries, and workflow logic that enable AI solutions to provide accurate, reliable, and useful outputs. • Build employee-facing AI assistants that help users find information, answer questions, and complete routine tasks. • Configure chatbot knowledge sources using company documentation, policies, procedures, and other approved content. • Rapidly prototype solutions and iterate based on user feedback and business needs. • Continuously improve existing solutions to increase usability, adoption, and business value. • Measurement and Continuous Improvement • Define expected outcomes and success measures for solutions and pilots. • Track results such as time savings, productivity improvements, adoption rates, and process efficiency. • Document lessons learned and recommend opportunities for expansion or improvement. • Help establish repeatable methods for evaluating AI use cases across the organization. • Governance, Compliance, and Responsible Use • Ensure AI solutions align with HIPAA, privacy requirements, organizational security standards, and established governance practices. • Incorporate appropriate human review and oversight into workflows when needed. • Support documentation of solution design, data sources, and operational controls. • Promote responsible and secure use of AI technologies. • User Enablement and Adoption • Create simple user guides, reference materials, and training content. • Demonstrate solutions to end users and stakeholders. • Gather user feedback and incorporate improvements into future releases. • Support the adoption of AI-enabled ways of working across the organization. • Innovation and Practice Development • Stay current on emerging AI tools, platforms, and capabilities. • Recommend opportunities to pilot new technologies that may improve productivity or efficiency. • Contribute to the development of the AI Incubator's standards, practices, and operating model. • Help build a scalable AI automation practice from the ground up.