About
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. You will own well-scoped projects and collaborate with experienced team members and cross-functional partners. Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a plus. Key responsibilities include: 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. Qualifications include: 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. Applied ML and model development: Two or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, including experience building and evaluating models with real-world data. Data analytics: Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations. Experimentation: Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact. Collaborative delivery: Experience working with engineering, product, analytics, or business partners to deploy or apply data-driven solutions. Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus. Machine learning and modeling: Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis. Data processing and feature engineering: Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development. Analytics and experimentation: Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement. Technical tools and workflows: Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools. Ownership mindset: Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues. Business-impact orientation: Connects modeling and analysis to customer experience and measurable outcomes. AI-first builder mindset: Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality. Growth mindset: Learns quickly, seeks feedback, and continuously develops technical and business knowledge. Clear, collaborative communication: Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners. Our hiring process includes four stages: Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience. Technical Interview: Demonstrate your applied machine learning, analytical, and technical capabilities. Hiring Manager Interview: Meet with the hiring manager to discuss your background and fit for the role. Final Interview: Meet with our AI leadership for a final assessment.
Languages
- English
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