• Support students with varying levels of knowledge • Translate technical content into simple, practical explanations • Deliver classes exclusively via synchronous remote instruction • Guide students through exercise solutions • Clarify doubts about logic and statistics, data manipulation, Machine Learning models, and Python libraries • Encourage analytical thinking and student autonomy • Maintain class engagement • Adapt the teaching approach to the participants’ pace • Relate learning to real market situations

• Build deep understanding of political and nonprofit users through research and feedback loops to translate their needs into data-informed product requirements. • Leverage product metrics and user interactions to ensure that we identify the areas of greatest impact for clients in the product roadmap. • Partner with data science, analytics, and data engineering to define model goals, understand modeling constraints, and translate experimentation and data-pipeline needs into clear product direction. • Write clear requirements for ML/data features, ensure cross-functional alignment, and monitor production model performance to drive evidence-based product decisions. • Act as the connective tissue across DS, engineering, and go-to-market teams, communicating priorities and tradeoffs while fostering an inclusive, mission-driven culture. • Creating a culture of inclusion by generating more and better ideas through diversity of input.

• Help build our AI platform using MongoDB and Nest.js. • Coding (writing, reviewing, testing, deploying) is at the heart of what we do. • Participate in regular Scrum ceremonies and work closely with the team to integrate AI capabilities seamlessly into our application. • Help define our AI best practices and governance frameworks to ensure our AI usage is both effective and ethical. • Opportunity to shape our platform’s direction and experiment with new ideas.

• Design and build user-facing ML features that harness LLMs and generative AI to unlock new product capabilities • Partner with product, design, and ML research to prototype and deliver high-impact, ML-powered experiences • Own the technical architecture and implementation strategy for applied ML systems - balancing latency, observability, and iteration speed • Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways • Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance • Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org