Machine Learning Scientist

At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​

This is a hybrid position. You’ll divide your time between working remotely from your home and an office, so you should live within commuting distance. Hybrid teams may work in-office as much as a few times a week or as little as once a month or quarter, as decided by leadership. The hiring manager can share more about what hybrid work might look like for this team.

Our core machine learning team applies large-scale data to power various business areas, and our goal is to build cohesive navigation experience across channels and web pages, to help simplify and improve the shopping experience for everyday entrepreneurs across the world to quickly find what they need on our website. We will roll up our sleeves to build best in class of personalization to offer most relevant products and prices at the right time. Your contributions will affect millions of our customers and will have a direct impact on our business results. Whether you’re passionate about crafting highly scalable systems or developing seamless customer experiences, we have a role for you. Join us and build the future of software at GoDaddy!

• →Build, evaluate, and improve ML models for business optimisation -including demand modelling, elasticity estimation, conversion prediction, and revenue forecasting.
• →Develop and maintain production ML pipelines on AWS, ensuring models are reliable, scalable, and easy to extend across new products and markets.
• →Design and analyse controlled experiments (A/B tests, multi-armed bandits) and translate results into actionable business recommendations.
• →Contribute to code quality, technical standards, and collaboration within the team.

• 3 + years of hands-on experience in applied machine learning or data science in a production environment.
• Strong foundation in deep learning, recommendation systems, or large-scale statistical modelling - regression, forecasting, or causal inference.
• Proficiency in Python and the data science ecosystem.
• Experience designing and analysing experiments - A/B testing, multi-armed bandits, or online learning methods.
• Familiarity with AWS data services (Athena, S3) or equivalent cloud data platforms.
• Experience deploying ML systems in production -data pipelines, model serving, or monitoring.
• Strong interpersonal skills, ability to explain modelling findings to both technical and business stakeholders.

• Experience in pricing, recommendation systems, revenue optimisation, or demand forecasting.
• Familiarity with Bayesian methods, probabilistic modelling, or hierarchical models.
• Experience with Spark or other large-scale data processing frameworks.
• Prior work in e-commerce, marketplace, SaaS, or consumer internet environments.
• Experience with LLMs and GenAI -prompt engineering, fine-tuning, evaluation, or building agentic workflows.