TL;DR

Data Science Manager (Games Tech): Leading a team of data scientists and ML engineers to develop innovative data solutions that enhance gameplay, user engagement, and business outcomes. With an accent on building internal data tools and customer-facing data products. Focus on driving best practices in predictive modelling, reinforcement learning, and scalable ML infrastructure.

Location: Hybrid in London, United Kingdom with at least 3 days a week onsite in the central London office. Must be authorized to work in the UK without visa sponsorship.

Company

Aristocrat is a global leader in social casino games, part of the Product Madness family, with a world-class team creating top-grossing titles and offices worldwide.

What you will do

  • Mentor and develop a team of data scientists and ML engineers.
  • Drive data science best practices including A/B testing, predictive modelling, and reinforcement learning.
  • Own data science project execution from development to integration and outcome assessment.
  • Translate analytical insights into actionable recommendations for senior leadership.
  • Implement software engineering best practices for ML model development and internal tools.
  • Collaborate with product and engineering teams to integrate analytical solutions into games and platforms.

Requirements

  • Must be based in or able to work hybrid in London, UK with at least 3 days onsite.
  • Authorized to work in the UK without visa sponsorship.
  • 5+ years in data science with 2+ years in leadership roles.
  • Expertise in clustering, predictive modelling, reinforcement learning, and Bayesian statistics.
  • Experience in ML Ops, software engineering, and deploying ML models at scale.
  • Proficiency in Python and familiarity with data processing technologies (Kafka, Spark) and cloud platforms (GCP, AWS, Azure).

Culture & Benefits

  • Inclusive culture with a People First principle.
  • Global inspiring workplace recognized with industry awards.
  • Opportunities for career growth and development.
  • Hybrid work model with flexibility.