Senior Data Scientist - Creative Optimization

Senior Data Scientist, Creative 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 Creative Optimization product:

The Creative Optimization product is a DCO (Dynamic Creative Optimization) application built and maintained in-house at Choreograph by the Optimization team. It enables personalization of creative content at scale across multiple channels such as digital display, mobile apps, CTV, YouTube, video, and social.

This is no startup. The application is already serving tens of millions of ads and terabytes’ worth of media every day in real-time across the globe.

About this role:

We are looking for a Senior Data Scientist to co-develop a step-change feature: Algorithmic Content Optimization (ACO). Our vision is to leverage data signals to algorithmically serve the most relevant creative to the right audience at the right moment, maximizing performance continuously.

The role reports to the VP of Data Science and is part of a small but growing team of Data Scientists.

The ideal candidate will have a background in Reinforcement Learning (or related disciplines), with hands-on cloud technology experience. While commercial experience is highly desirable, we are open to candidates with academic research in RL, given the deployment of RL at scale remains relatively nascent.

The candidate must be truly, technically competent, capable of customizing source code, adding new features, and coding from scratch, as off-the-shelf solutions often are not fit-for-purpose at this scale and complexity. Exposure to model deployment and software development will be sufficient, supported by our engineering team.

We seek a great team player passionate about applying Data Science techniques to solve complex problems and drive innovation. In return, you will solve cutting-edge problems and drive measurable performance improvements for our clients, working with a supportive team of seasoned developers, product managers, and data scientists who have built and deployed scalable, global products.

Key Responsibilities:

Develop and optimize the ACO algorithm(s) and related Data Science components for the product.

Design and contribute to the end-to-end machine learning pipeline, from data collection and reprocessing to model training, simulation, evaluation, deployment, and testing.

Implement and interpret explainability frameworks to ensure transparency and compliance with WPP standards.

Collaborate with stakeholders to identify business needs and translate these into scalable, impactful technical solutions.

Conduct rigorous model testing and validation for robustness and accuracy.

Prepare detailed documentation and reports to communicate complex model behaviors, predictions, and insights to both technical and non-technical audiences.

Stay updated on academic research and industry advancements in RL and AI/ML.

Share knowledge and support the wider team and Data Science community to foster innovation based on your work.

Essential Qualifications:

Bachelor's or master's degree in Data Science, Computer Science, Engineering, Statistics, or related quantitative field.

Hands-on (academic or commercial) experience in implementing Reinforcement Learning or related disciplines. Completing a module or thesis on RL during a degree is not considered sufficient; we value experience conducting original research, such as in an MRes, PhD, or fellowship.

Proficiency in Python and SQL.

Experience with Cloud technologies (GCP preferred but others are acceptable).

Experience or exposure to model deployment and/or software development.

Strong statistical and machine learning knowledge.

Effective communication skills for working with stakeholders of varying technical knowledge.

A collaborative team player attitude.

Highly Desirable Qualifications:

Research degree (MRes or PhD) with a thesis on Reinforcement Learning or related discipline.

Knowledge or experience in Causal Inference.

Commercial experience in implementing and deploying RL or similar personalization systems.

Experience in software development and ML Ops in a commercial setting.

Note: This is a UK-based role and requires individuals to have the right to work in the UK.

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