• Build advanced recommendation models, such as Next Best Offer systems, leveraging cutting-edge techniques. • Create and own the end-to-end lifecycle of machine learning models, from data preprocessing and feature engineering to production deployment. • Design and deploy scalable machine learning architectures that integrate seamlessly into customer-facing applications. • Continuously evaluate and improve model accuracy, scalability, and computational efficiency. • Stay up-to-date with the latest advancements in machine learning to ensure best-in-class solutions are applied to business problems. • Report to the Director of Growth Data Science. • Work with the Growth Data Science team to drive measurable impact on user engagement and business outcomes. • Collaborate with cross-functional partners in Product, Engineering, Analytics, and Marketing to deliver data-driven solutions. • Identify new opportunities to uncover actionable insights from large-scale datasets. • Grow the effectiveness of the PLG funnel through optimized model performance and personalization.

• Shape and implement the company's data science strategy and drive innovation. • Identify opportunities to leverage data for business value and impact. • Take ownership of the full technical stack for development and deployment. • Design, build, and maintain scalable machine learning models and efficient data pipelines. • Ensure robust data management and engineering practices. • Collaborate with cross-functional teams to gather requirements and deliver insights. • Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

• Leverage advanced algorithms and machine learning techniques to transform visual data into actionable insights • Design and implement robust models to analyze and interpret images and videos • Collaborate closely with cross-functional teams to integrate these capabilities into products • Preprocess large datasets and deploying machine learning frameworks • Conduct experiments to refine model accuracy and present findings to stakeholders

• Design and develop ML models (e.g., classification, ranking, scoring) that power customer-facing features • Use SQL and Python (pandas, scikit-learn) to analyse product data and identify opportunities • Rapidly prototype new AI/ML-driven product ideas in collaboration with Product and Design • Define hypotheses, evaluate model performance, and iterate quickly • Collaborate with Engineering to productionise validated models • Improve automation, personalisation, and predictive capabilities within the product • Ensure models are measurable, interpretable, and aligned with user value • Assess and validate internal and external data sources used in ML features • Evaluate signal quality, bias, completeness, and reliability • Define metrics and checks to ensure data is fit for customer-facing use • Move from ambiguous problem → prototype → shipped feature with strong ownership