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- Tasks: Lead a team of data scientists to create innovative data solutions for gaming.
- Company: Join Product Madness, part of the Aristocrat family, known for top-grossing games.
- Benefits: Enjoy a competitive salary, career growth, and a supportive, inclusive culture.
- Why this job: Make a real impact in the gaming industry with cutting-edge data science.
- Qualifications: PhD or MSc in Data Science or related field, with 5+ years of experience.
- Other info: Hybrid work model with at least 3 days in our vibrant London office.
The predicted salary is between 48000 - 72000 ÂŁ per year.
As the Manager of Data Science, Games Tech, you will be a transformational leader, responsible for guiding and inspiring a dedicated team of data scientists and machine learning engineers. In this role, you’ll drive the creation of groundbreaking data solutions that enhance gameplay, improve user engagement, and optimize business outcomes. As a key partner for multi‑functional teams, including game developers, data analysts, product, and game operations managers, you will use your ML and data expertise to build internal data tools that support decision making. You will also develop customer‑facing data products that enable personalized experiences in our industry‑leading games.
What You’ll Do
- Key Leadership Responsibilities
- Mentorship & Development: Provide ongoing mentorship, coaching, and professional development opportunities to foster growth and enhance team performance.
- Partnerships: Act as a trusted partner across the organisation, advocating for data‑driven decision‑making and empowering business units to adopt data products.
- Ownership & Accountability: Assume full accountability for the data science project execution to final integration and outcome assessment, ensuring that your team delivers impactful results on time and within scope.
- Insight Communication: Translate sophisticated analytical insights into actionable recommendations, communicating them to the senior leadership team to advise critical business decisions, with the ability to encourage and influence stakeholders.
- Data Science Best Practices: Drive best practices in A/B‑testing, predictive modelling, user clustering and reinforcement learning, to continually set the standard on data science benefit.
- Engineering Best Practices: Be responsible for the implementation of the best software engineering practices for internal tools and ML/RL model development, define software architecture standards, implement code review practices, auto‑tests, improve observability, reproducibility and monitoring of ML/RL solutions.
- Infrastructure Ownership: Own the development of analytical frameworks, including A/B testing (using Bayesian Inference and contextual multi‑armed bandits techniques) and other data science tooling, ensuring scalability, accuracy, and reliability across projects.
- Product & Engineering Collaboration: Coordinate integration of analytical solutions into games and platforms, partnering closely with product and engineering to ensure end‑to‑end solution success.
What We Need From You
- Expertise in clustering, predictive modelling, reinforcement learning, and Bayesian statistics.
- PHD or MSc or equivalent experience in Data Science, Computer Science, Statistics, Physics or related field.
- 5+ years of Data Science experience with a minimum of 2 years in a leadership role.
- Practical experience in software engineering, proven track record in design and development of customer‑facing products.
- Experience in ML Ops and deploying machine learning models at scale.
- Proficiency in Python, and familiarity with data processing technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure).
- Ability to work on a hybrid work basis requiring at least 3 days a week in our central London office.
Why Product Madness?
As part of the Aristocrat family, we share their mission of bringing joy to life through the power of play, with a world‑class team who creates top‑grossing, leading titles in the social casino genre. With 800 team members across the globe, Product Madness is headquartered in London, with offices in Barcelona, Gdańsk, Lviv, Montreal and a remote team spanning the USA, making us a truly global powerhouse. We live by our People First principle. Regardless of where, when, or how they work, our team members have opportunities to elevate their careers, and grow alongside us. We take pride in fostering an inclusive culture, where our people are encouraged to be their very best, every day.
Travel Expectations: None
Additional Information: At this time, we are unable to sponsor work visas for this position. Candidates must be authorized to work in the job posting location for this position on a full‑time basis without the need for current or future visa sponsorship.
Data Science Manager employer: Product Madness ??
Contact Detail:
Product Madness ?? Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Manager
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those that highlight your leadership and technical expertise. This will give you an edge when chatting with hiring managers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate insights effectively to various stakeholders.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our awesome team at Product Madness.
We think you need these skills to ace Data Science Manager
Some tips for your application 🫡
Show Your Passion: When writing your application, let your enthusiasm for data science and gaming shine through. We want to see how your passion aligns with our mission of enhancing gameplay and user engagement.
Tailor Your Experience: Make sure to highlight your relevant experience in data science, especially in leadership roles. We’re looking for someone who can inspire a team, so share examples of how you've mentored others or led successful projects.
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to explain your skills and experiences, making it easy for us to see how you fit into the role and our team.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss any important updates about the process.
How to prepare for a job interview at Product Madness ??
✨Know Your Data Science Inside Out
Make sure you brush up on your knowledge of clustering, predictive modelling, and reinforcement learning. Be ready to discuss how you've applied these techniques in past projects, especially in a leadership role. This will show that you not only understand the theory but can also implement it effectively.
✨Showcase Your Leadership Skills
Prepare examples of how you've mentored and developed your team in previous roles. Highlight specific instances where your guidance led to improved performance or successful project outcomes. This will demonstrate your ability to inspire and lead a dedicated team of data scientists.
✨Communicate Insights Clearly
Practice translating complex analytical insights into actionable recommendations. You might be asked to explain your thought process during the interview, so being able to communicate clearly and persuasively is key. Think about how you would present your findings to senior leadership.
✨Familiarise Yourself with Their Tech Stack
Research the technologies mentioned in the job description, like Python, Kafka, and cloud platforms. If you have experience with these tools, be prepared to discuss how you've used them in your work. Showing that you're already familiar with their tech stack will give you an edge.