• Lead the strategy and delivery of predictive models that improve customer acquisition, marketing performance and long-term commercial value • Shape capabilities including lifetime value modelling, propensity modelling, customer segmentation, forecasting and value-based bidding, ensuring every model is linked to measurable business outcomes • Partner with Marketing, Product and Commercial teams to apply Data Science to real business problems • Define how predictive analytics, experimentation and AI improve campaign performance, customer understanding and strategic decision making across platforms including Google and Meta • Ensure models become reliable, production-ready products rather than one-off analyses • Champion reproducible experimentation, scalable deployment, model monitoring, retraining strategies and continuous improvement throughout the model lifecycle • Lead and develop a growing team of Data Scientists while building trusted relationships across the business • Translate complex modelling into clear commercial recommendations, influence senior stakeholders through evidence, and establish Data Science as a trusted driver of business strategy and commercial growth • Represent Forbes in strategic conversations with technology partners including Google and Meta while staying connected to advances in AI, machine learning and marketing science • Evaluate emerging technologies, bring new ideas into the organisation and help ensure Data Science capability remains commercially relevant and technically leading

• Unified reporting and analytics: Actively build and maintain reports and dashboards that give the CS organisation clear visibility into key performance metrics, trends, and performance across channels, teams and projects • CS Systems configuration and data governance: Work closely with systems admins to advise on the structural setup of our Zendesk instance across customer-facing teams, ensuring it is configured to generate clean, consistent, and reliable data while maintaining secure systems and efficient workflows. • Data hygiene standards: Define and enforce standards for how data is captured across CS systems so that reporting is accurate, trustworthy, and reproducible • AI automation data oversight: Monitor and analyse data from our AI chatbot, ensuring it is tracked in a way that supports quality reviews and decision-making across the CS organisation • Insights to action: Translate raw data and reporting into clear recommendations that help CS leadership make decisions on resourcing, tooling, process improvements, and strategy • Stakeholder support: Partner with cross-functional partners in areas like Product, Data and Strategic Finance to understand their data needs and deliver reporting that supports their goals • Systems evaluation: Assess the data capabilities of existing and new CS tools, and make recommendations on how to optimise our systems stack for better reporting outcomes • Process documentation: Document data structures, reporting methodologies, and system configurations to ensure institutional knowledge is retained and accessible across the team

• Gather technical requirements and propose efficient data solutions, aligning data science with business objectives; • Define, design and implement data models to meet and exceed business area requirements; • Develop high-quality Data Science solutions, including supervised and unsupervised models, predictive models and deep analyses that address the company's primary challenges; • Independently execute all stages of the DS solution lifecycle (from understanding the business, studying and preparing features, to building and validating model performance); • Work at the technological forefront on projects involving Generative Artificial Intelligence, focusing on the creation of intelligent agents; • Collaboratively design and implement practical use and continuous monitoring of deployed models in partnership with business areas; • Assist in developing and maintaining data engineering pipelines, using best practices for organizing and optimizing workflows; • Develop secure, consistent and scalable solutions for data collection, transformation and quality assurance in data pipelines; • Investigate opportunities and propose new techniques while keeping technical documentation of solutions up to date; • Work horizontally and cross-functionally, showing genuine interest in understanding business processes and metrics; • Communicate effectively and concisely (oral and written), translating complex technical topics for different audiences and stakeholders.

• Conduct descriptive data analysis using case, survey, and event data to identify and size problems. Collaborate with cross-functional partners to identify potential improvements • Develop and implement key metrics to measure the effectiveness and impact of our products and services • Apply AI/ML techniques in collaboration with cross-functional teams to improve the delivery of products and services • Analyze administrative and public data to estimate the impact of proposed changes. • Create and maintain dashboards and visualizations to communicate key insights to stakeholders • Build and maintain evaluation pipelines to monitor AI system performance and turn results into prompt, model, and design improvements • Clean, combine, and make sense of diverse administrative data. • Investigate the barriers people face in applying for and keeping benefits. • Work with qualitative researchers to generate hypotheses about how to help our users. • Make use of past findings internally, from agencies, and from outside researchers to help strategically guide projects. • Work with program, product, engineering, and design to identify where our analytics and AI capacity will be most impactful. • Break down, estimate, and prioritize tasks with a good architectural/systems perspective • Lead and deliver on medium to large size projects • Create and maintain technical documentation for data science projects and models • Communicate project status, risks, and outcomes to technical and non-technical stakeholders • Mentor other data scientists on the team • Contribute to the development of data science best practices and standards • Other duties as assigned