Job Summary

The Data Scientist / Machine Learning Engineer will design, develop, validate, and operationalise AI/ML capabilities that support Content Management products and business outcomes. The role focuses on Content Insights, Ratings & Reviews AI, user-generated visual content pre-moderation, and image classification, helping create a sustainable data and AI foundation for content operations.

Key Responsibilities

AI/ML Solution Development

• Develop machine learning models and analytical solutions for Content Management use cases.
• Build AI/ML capabilities for image classification, content trend analysis, and user-generated content pre-moderation.
• Support Ratings & Reviews AI use cases, including summarisation and insight extraction where applicable.
• Design experiments and model evaluation approaches to validate model quality and business impact.

Data Science & Advanced Analytics

• Analyse content, behavioural, and operational datasets to identify patterns, trends, and improvement opportunities.
• Apply statistical analysis, ML techniques, NLP, and classification methods to solve Content Management problems.
• Use SQL, Python, BigQuery, and GCP-based tools to explore, transform, and model data.
• Support A/B testing and experimentation to measure the effectiveness of AI/ML-driven capabilities.

Machine Learning Engineering & Production Readiness

• Collaborate with Data Engineers and Software Engineers to integrate ML models into scalable pipelines and product platforms.
• Support model deployment, monitoring, retraining, and lifecycle management in GCP-based environments.
• Build maintainable ML components that can be operated beyond individual resource cycles.
• Contribute to repeatable and reliable approaches for moving models from experimentation to production.

Content Insights & AI Enablement

• Enable data-driven content insights that help improve content performance and operational decision-making.
• Support AI-enabled workflows for moderation, classification, summarisation, and analysis of content data.
• Partner with product and data teams to prioritise AI/ML use cases based on value, feasibility, and readiness.
• Contribute to the broader sustainable data and AI foundation for Content Management.

Cross-functional Collaboration

• Work closely with Product Owners, Data Engineers, Software Engineers, Product Specialists, and business stakeholders.
• Translate business problems into practical analytical and machine learning solutions.
• Communicate model insights, limitations, assumptions, and outcomes clearly to technical and non-technical stakeholders.
• Support cross-functional initiatives across stakeholder and related domains where required by roadmap priorities.
• AI/ML models and analytical solutions support Content Insights, R&R AI, UGVC pre-moderation, and image classification outcomes.
• Models are evaluated, documented, and integrated into sustainable data and product workflows.
• AI-enabled capabilities improve content operations, moderation, classification, summarisation, or insight generation.
• Collaboration with data, engineering, and product teams results in production-ready, maintainable solutions.
• Business stakeholders receive clear, actionable insights from model outputs and analysis.
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Skill Requirements

Required Skills & Experience

• Senior-level experience in Data Science, Machine Learning Engineering, or applied AI roles.
• Strong hands-on experience with Python and SQL.
• Experience with BigQuery and Google Cloud Platform (GCP).
• Strong knowledge of machine learning, NLP, image classification, and model evaluation.
• Experience with A/B testing, experimentation, and data-driven validation approaches.
• Ability to work with large datasets and build scalable analytical or ML solutions.
• Experience collaborating in Agile, cross-functional product teams.
• Business-level English communication skills.

Preferred / Nice-to-Have Skills

• Experience with production ML pipelines, MLOps practices, model monitoring, and retraining workflows.
• Experience with content analytics, CMS/DAM ecosystems, ratings & reviews, or user-generated content workflows.
• Exposure to computer vision, text summarisation, moderation models, recommendation systems, or content trend analysis.
• Experience working with cloud-native AI/ML services and scalable data platforms.
• Experience working with global stakeholders while operating primarily in CET hours.

Other Requirements