Senior Data Scientist - Game Analytics

Rockstar Games
Leeds
5 days ago
Create job alert

Join to apply for the Senior Data Scientist - Game Analytics role at Rockstar Games


At Rockstar Games, we create world‑class entertainment experiences. Become part of a team working on some of the most rewarding, large‑scale creative projects to be found in any entertainment medium - all within an inclusive, highly‑motivated environment where you can learn and collaborate with some of the most talented people in the industry.


Rockstar is on the lookout for a passionate Senior Data Scientist who possesses a passion for both games and big data. This is a full‑time, permanent, in‑office position based in Rockstar’s unique game development studio in the heart of Leeds, England.


What We Do

  • Provide actionable insights to a wide variety of stakeholders across the organization in support of decision making.
  • Partner with multiple departments across the company to design and implement data and pipelines.
  • Collaborate as a global team to develop cutting‑edge data pipelines, data products, data models, reports, analyses, and machine learning applications.
  • The Game Analytics vertical is heavily focused on understanding our players and using data to improve our games.

Responsibilities

  • Design, develop, and deliver data analytics solutions to address critical business or game questions, leveraging machine learning and advanced analytical techniques as appropriate.
  • Assure Rockstar’s ongoing competitive advantage by providing high quality insights and in‑depth analyses, aligned to strategic initiatives and design intentions.
  • Tell impactful stories with data through insightful, actionable reports and presentations.
  • Develop a deep understanding of gameplay flows, and leverage this to inform analysis of game data.
  • Identify and lead analytic value‑add projects and experiments aligned with long‑term strategic initiatives.
  • Conduct proactive in‑depth analysis and predictive modeling to uncover hidden opportunities.
  • Partner with data analysts, data engineers, data scientists, data governance analysts, and stakeholders to better understand requirements, find bottlenecks, and implement resolutions.
  • Help mentor and develop the skillsets of the junior team members within your team or department.

Requirements

  • 5+ years in a data science role.
  • Experience in writing production‑stable code (preferably Pyspark), pushing models and data pipelines to production, and iterating on models in production.
  • Proficiency in statistical theories such as probability, distributions, Bayesian statistics, causal inference.
  • Knowledge of machine learning techniques such as clustering, gradient boosting, neural networks, regression.
  • Excellent SQL skills and experience using Python for machine learning / statistical analysis.
  • Experience using large, complex datasets and building dashboards using a BI platform, ideally Tableau.
  • Excellent data storytelling and visualization skills, able to effectively communicate insights to a diverse range of stakeholders.
  • Strong problem‑solving skills with the ability to reconcile technical and business perspectives.
  • Passion for video games and knowledge of the industry.

Pluses

  • Experience with Databricks and MLFlow.
  • Graduate degree (MBA, MSc or Master’s, PhD), an asset.
  • Game industry experience, an asset.
  • Bachelor’s degree in Computer Science, Mathematics, or a related field, with a strong quantitative background.

How to Apply

Please apply with a CV and cover letter demonstrating how you meet the skills above. If we would like to move forward with your application, a Rockstar recruiter will reach out to you to explain next steps and guide you through the process.


Rockstar is committed to creating a work environment that promotes equal opportunity, dignity and respect. In line with this commitment, Rockstar will provide reasonable accommodations to qualified job applicants with disabilities during the recruitment process in order for such applicants to be considered for the position for which they are applying, as well as to qualified employees to enable them to perform the essential functions of their roles. If you need more information about Rockstar’s reasonable accommodation policies or process, or need to request an accommodation, please notify your recruiter during the interview process.


If you’ve got the right skills for the job, we want to hear from you. We encourage applications from all suitable candidates regardless of age, disability, gender identity, sexual orientation, religion, belief, race, or any other protected category.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.