Role Description We are continuously looking for experienced professionals who want to support and guide our students on their path to becoming professional developers. This is a full-time teaching role with an expectation of 40 hours per week. The schedule can be flexible, but you must be consistently available to support students throughout their learning journey. What you will do - Host regular live Q&A sessions to help students unblock and progress confidently. - Offer personalized support through 1:1 video calls to answer questions and guide assignments. - Occasionally respond to student questions via direct messages. - Lead live lessons on core program topics and industry-relevant skills. - Share your professional insights and best practices to support the learning community. - Review student projects and provide feedback based on program standards. - Write clear, actionable feedback to help students improve and meet expectations. - Support students in text by explaining concepts, troubleshooting code, and clarifying project or review questions. Qualifications - More than 2 years of experience working as a Data Scientist. - Experience working as an analyst using Python. - More than 1 year of experience working on machine learning projects. - Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn. - Intermediate English level. - Native or advanced Spanish proficiency. Benefits - Training in mentoring and communication techniques. - The opportunity to teach while continuing your main job. - The chance to share knowledge and experience with developers within our community. - Cross-cultural collaboration experience through mentoring and webinar facilitation. - Remote collaboration with a schedule that is convenient for both you and the team. We do not focus on micromanagement. - A comfortable digital office environment. We use modern digital tools such as Miro, Notion, Zoom, and others to ensure smooth collaboration. - A diverse and close-knit team distributed across the U.S. and Latin America.

• Lead the design and implementation of scalable ML systems, including supervised, unsupervised, and LLM-based solutions • Translate research and prototypes into production-ready systems • Partner with stakeholders to identify high-impact AI/ML opportunities and define optimal technical approaches • Provide technical mentorship and contribute to team upskilling • Build and operate LLM pipelines, including prompt design, fine-tuning, and evaluation • Develop RAG-based systems using embeddings, vector stores, and retrieval strategies • Design evaluation frameworks, feedback loops, and datasets to continuously improve model performance • Create reusable tooling to accelerate experimentation, deployment, and monitoring • Own end-to-end ML lifecycle: data pipelines, training, deployment, monitoring, and iteration • Establish best practices for reproducibility, observability, CI/CD, and model versioning • Partner with platform/DevOps teams to ensure reliability and scalability • Promote responsible AI practices, including governance, fairness, and transparency • Lead cross-functional initiatives across data engineering, analytics, and AI/ML • Translate complex ML concepts into clear recommendations for technical and non-technical audiences • Collaborate with clients and internal teams to plan and deliver AI/ML solutions • Contribute documentation, frameworks, and shared best practices • Scope and lead complex AI/ML initiatives aligned to business outcomes • Align stakeholders and drive execution across teams • Establish clear success metrics and ensure delivery of high-impact solutions

• Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks • Implement data pipelines for ingestion, preprocessing, feature engineering, and model training • Containerize ML models and applications using Docker for scalable and reproducible deployments • Deploy and maintain ML solutions in cloud environments (AWS/Snowflake) • Optimize model performance, latency, and resource utilization for real-time or batch inference • Monitor and troubleshoot ML models in production, ensuring reliability and robustness • Сollaborate with Product, Engineering, Data, and business stakeholders to define project requirements and integrate ML models into production systems • Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness • Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate

• Engineer systems that anticipate and neutralize security threats before they reach infrastructure. • Design, train, and deploy machine learning models directly at the edge. • Bridge the gap between security intelligence and massive-scale data science. • Collaborate with red teamers and security engineers to turn attack data into proactive defense mechanisms.