Senior Data Scientist - Media Optimization

Title: Senior Data Scientist, Media Optimization
Location: London
Reporting to: VP, Data Science, Optimization
 

Who we are:

Choreograph is WPP’s global data products and technology company. We’re on a mission to transform marketing by building the fastest, most connected data platform that bridges marketing strategy to scaled activation.

We work with agencies and clients to transform the value of data by bringing together technology, data and analytics capabilities.  We deliver this through the Open Media Studio, an AI-enabled media and data platform for the next era of advertising.

We’re endlessly curious. Our team of thinkers, builders, creators and problem solvers are over 1,000 strong, across 20 markets around the world.

About the Media Optimisation product:

The Media Optimisation product is an AI-powered optimization platform that enables media traders to deploy complex buying strategies and deliver better outcomes for our clients, at scale.

This is no start-up. We’re already trusted by our clients to manage hundreds of millions of media spend.

At the heart of the product is our proprietary algorithms, developed in-house and refined by the team over time. These algorithms enable the platform to make budget and targeting adjustments that improve campaign performance, everyday, for everyone of our clients across the globe.

Our ambition as a Data Science team is to further extend such competitive advantage by innovating our algorithms and expanding into more digital advertising platforms.

About this role:

We are on the lookout for a Senior Data Scientist to join our team to further improve our proprietary algorithms, as well as to innovate the product with latest advances in ML / AI.

The role will report to VP Data Science, and be part of a small (but growing!) team of Data Scientists.

The ideal candidate will have a background in optimisation (algorithms, or related ML disciplines), strong statistical knowledge and hands-on cloud technology experience.

As our algorithms are all proprietary and built on in-house team, the candidate needs to be truly, technically competent, to be able to quickly learn a complex system, customise source code, add in new features and code from scratch.

Whilst model deployment / software development experience is very, highly desirable, we do have a team of engineers to support so exposure in this space will be sufficient.

Culture-wise, we’re looking for a great team player who is passionate about applying Data Science techniques to solve complex problems and drive innovation.

In return, you will get the opportunity to solve cutting-edge problems, and drive measurable performance improvement for our clients. Not to mention, working with a team of supportive, seasoned deverlopers, product managers and data scientists who have successfully built and deployed scalable, global products.

Key Responsibilities:

• Further improve existing algorithms and develop net new DS features
• Design and contribute to the end-to-end machine learning pipeline from data collection, reprocessing to model training, simulation, evaluation, deployment and experimentation / testing
• Implement and interpret explainability frameworks to provide clear insights into model decisions, ensuring transparency and compliance with WPP standards
• Collaborate with stakeholders to identify business needs and translate these requirements into technical solutions that are scalable and impactful
• Conduct rigorous model testing and validation to ensure robustness and accuracy
• Prepare detailed documentation and reports that communicate complex model behaviours, predictions, and insights in a manner accessible to both technical and non-technical audiences
• Stay abreast of academic research and industry advancements in optimisation, plus AI/ML in general.
• Knowledge-share and support the wider team and Data Science community to drive innovations based on your work

Essential qualifications:

• Bachelor's or master's degree in Data Science, Computer Science, Engineering, Statistics, or a related quantitative field
• Hands-on experience in optimisation algorithms (or a related displicine). Please note:

-          Wihlst we’d consider candidates with relevant academic background, we’d prefer those with hands-on, commercial experience solving practical problems with real-world data.

-          Completing a module / thesis on this topic as part of bachelor’s degree is not considered as sufficient academic experience. We’re primarily thinking about the experience of conducting an original piece of research as part of an MRes, PhD, fellowship, etc

• Proficiency in Python (and key ML frameworks) and SQL
• Experience of using cloud technologies.

-          We’re AWS-based but migrating to GCP soon. Experience in any of these two, or another mainstream service is fine.

• Experience of / exposure to model deployment and/or software development
• Strong, demonstratable statistical and machine learning knowledge
• Effective communication skills to work with different stakeholders / team members with varying degrees of knowledge in Data Science
• A collaborative team player

Highly Desirable qualifications:

• Hands-on experience in Airflow
• Commercial experience in software development
• Commercial ML Ops experience