• Identify, prioritize, and execute financial data cleanup tasks for a streamlined consolidation transition. • Proactively audit and analyze financial data to identify and resolve any financial discrepancies and issues that could hinder the consolidation process. • Oversee the transfer of all financial data, ensuring timely and accurate migration to meet consolidation deadlines. • Drive the financial data validation process, ensuring accuracy and consistency across multiple systems. • Manage the accounting configuration for the target database, with a focus on maintaining data integrity. • Complete all post-cutover accounting tasks, including final financial reconciliation and closeout. • Oversee the financial configuration of the target database, ensuring accuracy and integrity. • Partner with key stakeholders to evaluate non-standard policies and exercise independent judgment to drive integration and ensure alignment with consolidation goals. • Provide support where needed to ensure the consolidation process runs smoothly and efficiently. • Maintain consolidation-related spreadsheets, documents, and data, ensuring they are up to date and accessible for the team. • Collaborate with the team to ensure all consolidation goals are met within the designated time frame, maintaining progress and addressing any roadblocks.

• Develop, validate, monitor, and evolve statistical, predictive, and Machine Learning models to support strategic business initiatives • Explore, analyze, and interpret large volumes of structured and unstructured data, turning them into actionable insights for decision-making • Conduct analytical studies and build models to understand customer behavior, identify patterns, and generate business opportunities • Design and implement scalable analytical solutions, ensuring result quality, efficiency, and reliability • Perform exploratory data analysis, hypothesis testing, and impact assessments to support strategic decisions • Identify opportunities to optimize processes using Data Science, Statistics, and Artificial Intelligence techniques • Work with data from various internal and external sources, ensuring integration, consistency, and data quality • Collaborate with Business, Data Engineering, and Technology teams to understand requirements and translate challenges into analytical solutions • Monitor the performance of models in production and propose continuous improvements to ensure accuracy and efficiency • Research, evaluate, and apply new methodologies, algorithms, and technologies related to Artificial Intelligence, Machine Learning, and Analytics to promote innovation and advance data solutions.

• Lead the strategy and delivery of predictive models that improve customer acquisition, marketing performance and long-term commercial value. • Partner with Marketing, Product and Commercial teams to apply Data Science to real business problems. • Work closely with Engineering and ML Ops to ensure models become reliable, production-ready products. • Lead and develop a growing team of Data Scientists while building trusted relationships across the business. • Represent Forbes in strategic conversations with technology partners while staying connected to advances in AI, machine learning and marketing science.

• Lead the strategy, roadmap and engagement initiatives for the core data visualization and data preparation ecosystem (BigQuery, Looker, and Looker Studio). • Guide the adoption cycle and curation of conversational data solutions (conversational analytics/CA) as a layer that provides agility and technology agnosticism. • Design, track, and evolve the directorate's scorecard of value and efficiency indicators, translating telemetry and analytical logs (e.g., reuse rates, autonomy and usage fidelity) into clear executive reports. • Drive planned cleanup and decommissioning of obsolete or idle analytical products at the edge, reducing clutter and noise in the environment. • Oversee team-built products, including Environment Cost Monitoring and Auditing and an AI-based Tools Migration Accelerator. • Lead alignment rituals and problem-discovery sessions with data leaders to ensure the roadmap solves the Group's real analytical bottlenecks.