Data Scientist, Entain

• Bachelor's or Master's degree in Statistics, Applied Mathematics, Econometrics, or related quantitative field
• 2-4 years of practical experience applying statistical methods to business problems
• Strong foundation in statistics, hypothesis testing, regression analysis, and experimental design
• Experience with marketing mix modeling, incrementality testing, or causal inference
• Proficiency in Python for data analysis and statistical modeling (pandas, numpy, scipy, statsmodels)
• Familiarity with machine learning libraries such as scikit-learn
• Experience with SQL for data extraction and manipulation
• Demonstrated ability to translate analytical findings into business recommendations
• Track record of successful project delivery with measurable business impact
• Experience communicating technical concepts to non-technical audiences
• Understanding of business metrics and KPIs in a commercial setting

Desirable

• Data intelligence and AI plays a transformational role in how we create value today and how we shape our future
• We have a bold ambition to be a world-class data and AI driven business for our customers across all our regions and business functions
• This is a hands-on junior data scientist role that will work within our CILA Data & AI function
• You will apply statistical modeling techniques and machine learning to solve real business problems and derive actionable insights
• The purpose of this role is to leverage your strong statistical foundation to develop and implement models for marketing mix modeling (MMM), incrementality testing, A/B testing, and other business applications
• By combining your statistical expertise with practical Python skills, you'll help translate data into business value and drive data-informed decision making throughout the organization
• Design and implement statistical models to solve specific business problems, particularly in marketing and customer analytics
• Develop and optimize marketing mix models (MMMs) to measure effectiveness of marketing channels and campaigns
• Build incrementality testing frameworks to quantify true business impact of initiatives and interventions
• Create statistical models for A/B testing and experimentation, ensuring robust methodology and reliable results
• Collaborate with business stakeholders to understand their needs and translate them into analytical solutions
• Clean, transform, and prepare data for analysis using Python and SQL
• Interpret statistical results and communicate insights clearly to non-technical stakeholders
• Document methodologies, assumptions, and findings to ensure reproducibility and knowledge sharing
• Monitor model performance and refine approaches based on feedback and results
• Stay current with statistical and machine learning techniques relevant to business applications
• Partner with data engineers and other data scientists to implement models in production