• Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support. • Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business. • Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling. • Improve the ML development experience by building better operational patterns and advancing production-ready ML practices. • Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production. • Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems. • Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations.

• Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering • Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks • Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets • Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines • Work with structured and unstructured data at scale — including text, tabular, and time-series data • Monitor model performance in production and implement retraining and drift-detection strategies • Collaborate with engineering and product teams to translate data insights into actionable AI features • Document data schemas, model architectures, and pipeline logic clearly and thoroughly

• Lead dynamic live discussions that foster interaction and deepen understanding. • Deliver clear, constructive, and authentic feedback on student submissions, including recorded video responses. • Manage online discussions, respond promptly to student inquiries, and track student progress. • Facilitate a minimum of 1-2 courses per month with consistent engagement and preparation. • Complete an in-depth onboarding program, including shadowing live courses, participating in debrief sessions, and mastering the assigned certificate program. • Engage in ongoing training and professional development to stay current with emerging learning methodologies, educational technologies, and best practices in online facilitation.

• Build and scale experimentation services • Develop RESTful APIs using FastAPI • Collaborate with data scientists and product teams • Optimize data processing using PySpark