Data Scientist - Decisioning (Pega Focus)
Location: Remote (UK-based)
We are partnering with a prestigious and rapidly expanding professional services firm that is projecting significant growth, targeting an expansion from 50 to 200 employees over the next two to three years. We are urgently seeking a Senior Data Scientist to lead their focus on Pega decisioning within the highly regulated financial services sector.
This is a key consultative role that blends deep technical expertise with commercial insight, giving you the chance to deliver high-impact, customer-centric analytical solutions.
Key Responsibilities:
• Design and implement predictive models to power next-best-action and next- best-offer logic.
• Develop and implement simulation and optimisation models to inform optimal intervention strategies and evaluate portfolio-level impact.
• Work within regulated environments, ensuring fairness, transparency, and explainability of model-driven decisions.
• Integrate customer factors like eligibility, consent, and vulnerability into decision logic.
• Engage senior stakeholders to translate business requirements into robust, actionable analytical approaches.
What You'll Bring
We are seeking a commercially aware and practical Data Scientist who thrives in a consulting environment.
Essential Experience & Skills:
• Experience developing and deploying predictive models and decisioning logic (e.g., credit risk, customer marketing) in the financial services sector.
• Experience with an Pega's enterprise-level decisioning platform, including integrating predictive models, optimizing arbitration engines, and monitoring performance.
• Minimum 2:1 degree in a STEM subject (MSc or postgraduate qualification in a quantitative discipline is preferred).
• Strong working knowledge of statistics, including experiment design and predictive modeling.
• Proficiency in Python (preferred) and a data manipulation language like SQL (preferred), ideally in a cloud environment (AWS, Azure, or GCP).
• Strong understanding of profit and loss levers and their interplay with customer behaviour.