Data Scientist
WPP Media is seeking a Data Scientist to design, develop, and deploy data modeling and automation solutions using AI and ML techniques. This role involves hypothesis testing, data preparation, model development, and collaboration with engineers and business teams to deliver actionable insights.
- Compensation
- Not disclosed
- Location
- London, United Kingdom
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Proven experience in applied data science, analytics, or ML engineering.
- Strong SQL skills and comfort working with large-scale, multi-source data.
- Experience with version control (Git) and collaborative coding workflows.
- Advanced Python skills, incl. pandas, numpy, scikit-learn, scipy, statsmodels for modelling and analytics.
- Experience in building and execution of advanced solutions for audience analytics and insights.
- Strong analytic and problem-solving skills, understanding and experience in application of AI methodologies for data manipulation and insight.
- Experience with data visualization platforms beyond Power BI/Tableau.
- Sufficient knowledge of data governance practices including data QA, general naming taxonomy, data & platform integration, etc.
- Ability to communicate analytical findings to non-technical stakeholders clearly and impactfully.
Nice to have
- Bachelors or advanced degree in Statistics, Economics, Business, Math, or Sciences.
Full description
About WPP Media
WPP is the trusted growth partner for the world’s leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by our pioneering agentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth.
WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working.
For more information, visit wppmedia.com.
2141 - Data Scientist
Reporting to: Solution Architect
Role Summary and Impact :
The Data Scientist reports to Solutions Architect and is responsible for designing, developing & deploying data modelling and automation solutions for a leading client across markets, the portfolio of brands. You should have a strong understanding of the concepts and tools in data enrichment, automation and agentic space. You focus on methodological depth and the correct application of data science techniques. You translate complex business questions into robust data models and automation workflows and work closely with engineers to bring these models into production in a scalable and maintainable way. You'll be part of a team that believes in doing work that matters - for people and the planet, extending this impact directly to our client's strategic objectives.
What will you do :
- Actively explore and identify the latest techniques related to DS, ML and automation/agentic techniques.
- Perform hypothesis testing, data preparation and product prototyping based on modern statistical and DS/ML and agentic methods.
- Research, develop and apply DS/ML/agentic models to solve business and clients’ problems effectively.
- Use your knowledge to anticipate, identify, and address data issues related to redundancies, quality, and bias. Apply rigorous data cleaning and preprocessing techniques to ensure high-quality data for modelling.
- Lead data science projects, blending various data science techniques as needed to achieve optimal performance and actionable insights. Continuously explore innovative and novel approaches to improve modelling accuracy and efficiency.
- Collaborate directly with internal analytics and business teams, to understand and document requirements, and deliver data science solutions that meet their specific business needs.
- Monitor data quality, troubleshoot issues, and implement improvements to ensure data reliability.