AI & Machine Learning Engineer I

Skills

About the Role

You will own well-scoped machine learning projects from data exploration through model development, validation, deployment, and iteration. You will build and improve predictive, recommendation, ranking, segmentation, uplift, and customer-value models to support personalization and decisioning. You will prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation. You will design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact. You will collaborate with engineering, product, analytics, and business partners to integrate your models into production and refine them based on results and feedback. You will also use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of your modeling and analytical workflows.

Requirements

• Two or more years of professional experience in applied machine learning data science ML engineering or applied statistics
• Experience building and evaluating models with real-world data
• Experience analyzing behavioral transactional product marketing or customer data
• Experience defining success metrics analyzing experiments and evaluating model performance
• Experience working with engineering product analytics or business partners to deploy data-driven solutions
• Strong Python skills and practical knowledge of supervised learning model selection hyperparameter tuning and evaluation
• Strong SQL skills and experience using platforms such as BigQuery Spark or similar tools
• Strong analytical and statistical reasoning including A/B testing and statistical significance
• Familiarity with common ML libraries cloud data or ML platforms version control and AI-assisted development tools
• Experience with personalization recommendation ranking uplift modeling causal inference contextual bandits pricing or lifecycle decisioning is a plus

Responsibilities

• Own well-defined machine learning projects from data exploration through validation deployment and iteration
• Build and improve predictive recommendation ranking segmentation uplift and customer-value models
• Prepare datasets define modeling targets develop features and ensure data quality
• Design and analyze A/B tests holdouts and offline evaluations to measure performance and business impact
• Collaborate with engineering product analytics and business partners to integrate models into production
• Use AI coding assistants automation and reusable tools to improve modeling and analytical workflows

Benefits

• Flexible working options
• Time off
• Well-being programs
• Bonus