Role Description Wir suchen engagierte und kompetente freiberufliche Dozierende (m/w/d) für den Kurs JavaScript die unser Team im Rahmen von Vertretungseinsätzen in verschiedenen Fachbereichen unterstützen. Bei Interesse an einem festangestellten Verhältnis verweisen wir auf unsere anderen Stellen. - Fachlich fundierte und praxisnahe Vermittlung von Lerninhalten. - Kurz- und mittelfristige Übernahme von Lehrveranstaltungen in Vertretungssituationen. - Betreuung und Unterstützung von Teilnehmenden. Qualifications - Nachweisbare, sehr gute JavaScript-Kenntnisse. - Erfahrung als Software-/Webentwickler mit entsprechenden Frameworks und praktische Umsetzung in Projekten. - Relevante Berufserfahrung in der Anwendung von JavaScript in der Webentwicklung. - Praxiserfahrung in der Erwachsenenbildung. - Freude an der Wissensvermittlung und didaktisches Geschick. - Kommunikationsstärke, Empathie, technische Versiertheit. - Selbstständige Arbeitsweise und Flexibilität. - Sicherer Umgang mit digitalen Tools und Lernplattformen. Benefits - Remote Work: Du kannst deutschlandweit zu 100 % mobil arbeiten. - Kurze Abstimmungswege: Unkompliziert und schnell im Austausch mit uns.

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio • Build production-grade systems that combine predictive, causal, and generative AI capabilities • Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines • Collaborate closely with data engineers, ML engineers, analysts, and platform teams • Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems) • Apply statistical, predictive, and prescriptive modeling techniques to solve business problems • Build reusable modeling frameworks that can scale across multiple domains • Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods) • Translate observational and experimental data into actionable business insights • Embed causal reasoning into decision systems that guide actions

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio. • Work at the intersection of data science, machine learning, and GenAI, turning complex data into actionable insights, automated decisions, and intelligent workflows. • Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines. • Collaborate closely with data engineers, ML engineers, analysts, and platform teams, contributing to shared modeling standards and cross-functional AI architecture. • Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems). • Apply statistical, predictive, and prescriptive modeling techniques to solve business problems. • Build reusable modeling frameworks that can scale across multiple domains. • Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods). • Translate observational and experimental data into actionable business insights. • Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs). • Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows. • Contribute to the development of intelligent agents and AI-assisted decision-making systems. • Combine structured data models with unstructured data and GenAI outputs.

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases • Build production-grade systems that combine predictive, causal, and generative AI capabilities • Turn complex data into actionable insights, automated decisions, and intelligent workflows • Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI • Collaborate closely with data engineers, ML engineers, analysts, and platform teams