Role

We're seeking a technically proficient and commercially minded Machine Learning Engineer to join our Product Listings team. The focus is on enabling smarter, scalable advertising through automation and optimization.

This hybrid role combines deep data science expertise with production-grade ML engineering. You will drive performance marketing automation by designing experiments, building predictive models, and deploying them at scale to optimize product bidding and maximize profits across platforms like Google Shopping.

You will work across the entire ML lifecycle—from exploring datasets and engineering features to evaluating models and running validation experiments. Additionally, you will build, deploy, and monitor models in production, setting up retraining workflows, pipeline orchestration, and performance alerts, supported by your tech lead and colleagues from the Data Science chapter at Lyst.

Our main tools include Python with frameworks like SKLearn, TensorFlow, and PyTorch, operating within AWS Sagemaker where possible. We emphasize clean, documented, well-tested, and reviewed code, supported by our tooling and culture.

This role offers a hands-on, high-impact opportunity that blends research, experimentation, and engineering, all tied to clear business outcomes. You will collaborate closely with marketers, data analysts, and engineers to shape the future of scalable advertising.

Responsibilities

• Productionize ML models into robust, maintainable systems, including retraining pipelines, monitoring, and performance alerts.
• Build and evaluate ML models supporting smarter paid marketing, such as ROAS prediction, lifecycle-aware bidding, and grouping optimization.
• Explore large-scale datasets to uncover insights, test hypotheses, and identify features that drive business value.
• Handle all aspects of the data science workflow—from data cleaning and analysis to offline benchmarking and validation.
• Collaborate with marketers to reduce manual campaign management overhead while improving ad efficiency and profitability.
• Enhance our ad feed infrastructure to optimize product attributes, pricing, and availability for campaign performance.
• Contribute to product data initiatives, especially where they impact ad quality or model inputs.

Minimum Requirements

• 3+ years of experience building and deploying ML models in production.
• Strong Python and SQL skills, with expertise in data wrangling, feature engineering, and model evaluation.
• Deep understanding of the data science process, including exploratory analysis, statistical testing, and model comparison.
• Experience with structured prediction problems using real-world, messy data.
• Familiarity with advertising systems (Google Shopping, PMAX) and marketing metrics like ROAS, profit, and conversion is advantageous.
• Experience in productionizing ML models, including training pipelines, versioning, monitoring, and retraining.

We also offer a flexible working environment, comprehensive benefits, and a commitment to diversity and inclusion. We encourage candidates from all backgrounds to apply, even if they don't meet every requirement.