Principal Data Scientist

Product Madness, Inc.; Greater London; On-site; GBP 120,000 - 160,000; Full time

Job summary

Aristocrat is hiring a Principal Data Scientist in London to lead end-to-end data science initiatives that enhance gameplay and business outcomes. You will develop ML and reinforcement learning solutions aimed at improving player engagement, retention, and monetization.

You will shape the modeling framework, ensure rigorous evaluation and monitor causal inference, uplift, and forecasting. Collaboration with game teams, data engineering, and MLOps will be essential to deploy scalable data

Qualifications

• PhD or MSc in Data Science, Computer Science, Statistics, Physics, Mathematics, or related quantitative field, or equivalent experience.
• 5+ years of professional data science experience with 3+ ML products
• Proficiency in clustering, predictive modeling, RL, Bayesian statistics
• Hands-on software engineering, MLOps and deploying ML models at scale
• SQL, Python; familiarity with big data tools (Kafka, Spark) and cloud platforms

Responsibilities

• Lead end-to-end data science initiatives from framing to impact assessment.
• Build ML and reinforcement learning solutions to boost engagement, retention and monetization.
• Define and monitor modeling frameworks including causal inference and uplift modeling.
• Collaborate with game teams to translate insights into product actions.
• Establish engineering standards for model development and deployment.

Skills

PhD/ MSc in data science

Production ML

SQL

Python

MLOps

Cloud platforms

Reinforcement learning

Big data technologies

Gaming knowledge

Education

PhD or MSc in Data Science / quantitative field

Tools

Kafka

Spark

AWS / GCP / Azure

Git & CI/CD

Job description

Are you ready to join a world-class team and make a significant impact on the gaming industry? At Aristocrat, we aim to bring happiness to life through the power of play. We seek a Principal Data Scientist to help us reach our ambitious goals. You will have a vital role in enhancing gameplay, boosting player engagement, and improving business outcomes with your advanced data expertise. This opportunity allows you to work on innovative projects, collaborate with diverse teams, and guide critical initiatives that will develop the future of our leading games.

What You'll Do

• Lead high-impact data science initiatives end-to-end, including problem framing, methodology selection, experiment development, implementation partnership, and impact measurement.
• Build and deliver machine learning and reinforcement learning solutions to improve player engagement, retention, monetization, and operational outcomes.
• Lead the modeling framework for complex systems, guaranteeing comprehensive evaluation and monitoring of causal inference, uplift modeling, sequential decisioning, bandits/reinforcement learning, and forecasting.
• Partner with game teams to define success metrics, guardrails, and decision frameworks, translating analytical results into actionable product and operational actions.
• Define and uphold engineering standards and guidelines for model development, including validation, uncertainty, reproducibility, and bias/quality checks.
• Drive scalable experimentation with A/B and Multi-armed bandit testing frameworks, power analysis, variance reduction, and online-offline alignment.
• Work together with Data Engineering, MLOps, and Game Tech teams to guarantee dependable data foundations, feature accessibility, and model deployment pathways.
• Build internal data products to improve the speed and quality of decision-making, such as AB-test calculators, decision tools, and automated insights.
• Provide technical leadership through building and code reviews, mentoring, and coaching, improving the standard of data science craft across the organization.
• Serve as a reliable collaborator throughout the organization, promoting data-informed decision-making and enabling business units to embrace data products.
• Translate complex analytical insights into actionable recommendations, presenting them to senior leadership to inform critical business decisions and encourage collaborators.

What We Are Seeking

• Education: PhD or MSc in Data Science, Computer Science, Statistics, Physics, Mathematics, or a related quantitative field, or equivalent experience in practice.
• Experience: 5+ years of professional data science experience. You have delivered at least 3 data or ML products from problem definition to production deployment and monitoring.
• Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.
• Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale.
• Proficiency in SQL, Python, and familiarity with big data technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure).
• Industry knowledge: Experience in gaming or digital entertainment is a strong plus.

Esta descripción del puesto puede haber sido revisada y optimizada mediante herramientas de inteligencia artificial con el fin de mejorar su claridad, coherencia e inclusión. En cualquier caso, todo el contenido final, los requisitos del puesto y las decisiones de contratación están sujetos a la revisión y aprobación de Aristocrat.

Filosofía de compensación

Ofrecemos un paquete integral de remuneración y beneficios diseñado para seguir siendo competitivo en el mercado, apoyar tu bienestar y reconocer tu contribución a nuestro éxito. Nuestro enfoque se basa en una filosofía de remuneración por desempeño, recompensando el impacto individual. Tu paquete de compensación específico se determinará en función de factores como tus habilidades, experiencia, cualificaciones y ubicación.
Dependiendo de tu puesto y ubicación, podrás optar a bonificaciones e incentivos anuales, beneficios de salud y bienestar, tiempo libre remunerado, planes de jubilación, cobertura de seguros y otros beneficios locales o establecidos por la legislación aplicable.
Los detalles específicos sobre la compensación y los beneficios para este puesto se tratarán durante el proceso de selección.