Machine Learning Engineer I

About the role

Our team is a core part of Gen's AI transformation. We build machine learning solutions that improve customer growth, retention, personalization, pricing, recommendations, billing success, and long-term customer value. We are looking for a hands-on AI / Machine Learning Engineer I to build models, analyze customer and product data, evaluate experiments, and help deploy practical ML solutions.

Key Responsibilities

• Applied ML ownership: Own well-defined machine learning projects from data exploration and model development through validation, deployment, and iteration.
• Model development: Build and improve predictive, recommendation, ranking, segmentation, uplift, and customer-value models for customer personalization and decisioning.
• Data and feature development: Prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation.
• Experimentation and measurement: Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact.
• Deployment and collaboration: Work with engineering, product, analytics, and business partners to integrate models into production and improve them based on results and feedback.
• AI-first development: Use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of modeling and analytical workflows.

About You

• Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a plus.
• 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.
• A Master’s or PhD in a quantitative field is a plus, but not required.
• Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus.
• Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis.
• Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development.
• Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement.
• Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools.
• Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues.
• Connects modeling and analysis to customer experience and measurable outcomes.
• Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality.
• Learns quickly, seeks feedback, and continuously develops technical and business knowledge.
• Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners.