AI / Machine Learning Engineer II

Our team is a core part of Gen’s AI transformation. We build machine learning systems that directly improve customer growth, retention, personalization, pricing, recommendations, billing success, and long-term customer value across a large global consumer portfolio. This role focuses on applied machine learning, experimentation, and business-impact modeling. You will build practical models that personalize customer decisions across in-app messages, email, portals, billing flows, and lifecycle journeys. We are looking for a hands-on AI / Machine Learning Engineer who can frame business problems, build models, design experiments, measure impact rigorously, and partner with engineering and product teams to bring models into production. Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a strong plus.

Responsibilities

End-to-end ML ownership: Independently lead applied machine learning initiatives from data preparation and model development through experimentation, production deployment, monitoring, and continuous optimization; Productionization and MLOps: Deploy and operate scalable ML solutions with robust workflows for batch or real-time inference, evaluation, monitoring, observability, versioning, retraining, rollback, and continuous model iteration; Experimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure incremental customer and business outcomes; Advanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit, segmentation, optimization, and customer-value models; Cross-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business teams to integrate models into reliable production systems; AI-first engineering workflows: Build agentic tools, automation, and reusable modules that streamline model development and MLOps workflows, improve productivity, and increase the speed, quality, and consistency of ML delivery

Requirements

Five or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, or equivalent demonstrated impact; Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or customer data; Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and translating findings into practical product or business decisions; Experience partnering with engineering, product, analytics, and business teams to deploy and operate production ML systems, including inference pipelines, monitoring, observability, retraining, and cloud-based MLOps workflows; Strong Python skills and hands-on experience with common ML frameworks, supervised learning, model selection, hyperparameter tuning, evaluation, and performance diagnosis; Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data collection, cleaning, preprocessing, exploration, and feature development; Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal measurement, incrementality, statistical significance, and business-impact analysis; Experience with cloud ML platforms, deployment pipelines, batch or real-time inference, CI/CD, model registries, monitoring, observability, retraining, rollback, and scalable system design; Degree requirements are flexible; a technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued; Strong ownership; Business-impact orientation; AI-first builder mindset; Clear, collaborative communication

Nice to have

A Master’s or PhD in a quantitative field; Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization; Relevant specialization in personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning

Benefits

Flexible working options; time off; competitive pay; benefits; well-being programs