Senior Data Scientist - Game Analytics

Rockstar Games
Leeds
2 days ago
Create job alert

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 and in-office position based in Rockstar’s unique game development studio in the heart of Leeds, England.


WHAT WE DO

  • The Rockstar Analytics team provide actionable insights to a wide variety of stakeholders across the organization in support of their decision making.
  • We partner with multiple departments across the company to design and implement data and pipelines.
  • We 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 required.
  • 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

Please note that these are desirable skills and are not required to apply for the position.



  • 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.

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.