Design, develop, and implement advanced statistical models and machine learning algorithms (e.g., predictive analytics, natural language processing, time series forecasting, media mix modeling, attribution modeling, audience segmentation, propensity modeling) to address key business challenges in media and advertising. - Conduct in-depth data exploration and analysis to identify trends, patterns, and anomalies in large and complex datasets originating from various sources (e.g., ad servers, DMPs, CRM, web analytics, social media, walled gardens). - Collaborate closely with Cloud Engineers to ensure data quality, availability, and pipeline efficiency, and with other stakeholders (e.g., media planners, strategists, client service teams, product managers) to understand business needs and translate them into scalable data science solutions. - Communicate complex analytical findings, model results, and strategic recommendations clearly and concisely to both technical and non-technical audiences through compelling visualizations, presentations, and written reports. - Develop, deploy, and maintain robust, scalable, and production-ready data science models, working closely with MLOps and Data Engineering teams for seamless integration, monitoring, and performance optimization. - Stay abreast of the latest developments in data science, machine learning, artificial intelligence, and media measurement, evaluating and implementing new techniques and tools to continuously enhance our capabilities and deliver competitive advantage. - Contribute to the continuous improvement of data science practices, methodologies, documentation, and tooling within WPP Media, fostering a culture of innovation and data excellence. Requirements
Bachelor’s degree plus relevant industry experience, or a Master’s degree, in a quantitative field such as Statistics, Computer Science, Mathematics, Economics, Physics, Operations Research, or related fields. - Experience: 2-3 years of professional experience as a Data Scientist, ideally within the media, advertising, marketing technology, e-commerce, or consulting industries. - Proven experience with the full data science lifecycle, from problem definition, data acquisition and cleaning, exploratory data analysis, model development, validation, to deployment, monitoring, and iteration. - Demonstrated experience in applying statistical modeling, machine learning to solve real-world business problems with tangible impact. - Strong Python proficiency with a focus on building production-ready ML systems: clean architecture, reusable modules, testing, and pipeline orchestration. Hands-on experience with data processing (Pandas, NumPy), machine learning frameworks (Scikit-learn, XGBoost, LightGBM, CatBoost), PyTorch, and statistical modeling in production environments. - Expertise in SQL for querying, manipulating, and transforming large datasets in various database environments. - Understanding of MLOps principles, including model deployment, versioning, and monitoring. - Strong problem-solving and analytical skills, with solid algorithmic thinking and a curiosity to deeply explore data and modeling challenges. - Strong communication skills with the ability to convey complex technical concepts to diverse audiences.
