Senior Data Scientist - Creative Optimization
- 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, 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 RL, plus AI/ML in general.
- Knowledge-share and support the wider team and Data Science community to drive innovations based on your work
- Be Extraordinary by Leading Collectively to Inspire transformational Creativity.
- Create an Open environment by Balancing People and Client Experiences by Cultivating Trust.
- Lead Optimistically by Championing Growth and Development to Mobilize the Enterprise.
- Bachelor's or master's degree in Data Science, Computer Science, Engineering, Statistics, or a related quantitative field
- Hands-on (academic/commercial) experience in implementing Reinforcement Learning (or a related displicine). Please note:
- We use the term Reinforcement Learning as an umbrella term rather than a specialist term for state-dependent action set frameworks
- 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 SQL
- Experience of using Cloud technologies. GCP will be ideal, but other mainstream ones are fine as well
- Experience of / exposure to model deployment and/or software development
- Demonstrable 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
- Research degree (MRes or PhD) with a thesis on Reinforcement Learning or related discipline
- Knowledge / experience in Causal Inference
- Commercial experience in implementing and deployment RL or a similar personalisation system
- Commercial experience in software development
- Commercial ML Ops experience
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