Data Scientist 3

Overview:

Using data, you will play a crucial role in driving decision making and delivering impactful insights to our stakeholders at SIE. You will be responsible for applying advanced analytical techniques, building machine learning models, and using causal inference methods to solve sophisticated business problems related to platform engagement, product features, and first-party games!

What you’ll be doing:

• Apply advanced statistical and machine learning techniques to analyse large and complex datasets, extracting actionable insights in a commercial setting
• Apply your knowledge in causal inference, machine learning, and statistical modelling to develop robust predictive models and identify causal relationships
• Collaborate with cross-functional teams to define business problems, formulate hypotheses, and design experiments to test these hypotheses
• Independently extract and integrate data from multiple data systems (including writing complex SQL queries) to support analytical efforts
• Participate and support in delivering results and presentations to stakeholders
• Provide thought-leadership in ongoing financial measurement and analysis of SIE’s services; Identify new insights and opportunities for analytics projects!
• Promote and share the team's work, methods, and skills in data science, causal inference, and machine learning with team members and the broader SIE community
• Stay up to date on the latest advancements in the field of data science (including causal inference and machine learning) and share knowledge and new ideas with team.

What we’re looking for:

• Knowledge of gaming industry and relevant gaming titles
• 5+ years of advanced SQL required
• 5+ years of experience using statistical packages (R or Python) for model development
• Familiarity with common development tools and practices, including version control systems (e.g., Git) and workflow management tools (e.g., Airflow)
• Excellent communication and data storytelling skills, with the ability to effectively communicate technical concepts to non-technical partners
• Master's Degree or Ph.D. in Applied Math, Economics, Statistics, or Engineering preferred. BA/BS Degree in Mathematics, Applied Math, Statistics, Computer Science is a minimum requirement
• In-depth understanding and experience using supervised and non-supervised machine learning techniques
• Understanding of causal inference methods (such as propensity score matching, synthetic control methods, and difference-in-differences) for accurately estimating causal relationships
• Proficiency in designing and performing hypothesis tests to validate or reject research hypotheses
• Experience with standard BI Tools (Tableau, MicroStrategy, etc.).

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers