Data Scientist

**About Us:
**Betty is an innovative entertainment company pioneering at the intersection of real-money online casinos and casual mobile gaming.

Betty Canada is a scale up company accredited by the Alcohol and Gaming Commission of Ontario (AGCO) as a B2C operator in February 2023, we've set a new standard in the industry.

Our mission is to redefine the online casino experience by offering a uniquely transparent environment where players can relax, unwind, and enjoy themselves safely. We are committed to accessibility, fairness, and inclusivity, fostering a community of like-minded individuals who value ethical gaming practices and prioritize our players' safety and enjoyment above everything else.

Our Values:

• We are honest – we value honesty in all aspects.
• Bring the Olives – we offer premium customer experience.
• Think Big – we believe in always striving for more.

**
Key Responsibilities**

• Design, build, test, and deploy predictive and prescriptive models (e.g., churn prediction, player lifetime value, segmentation, risk classification, fraud detection).
• Analyze large-scale player behavioral data to identify key value drivers and recommend optimizations for promotions, engagement, and monetization strategies.
• Develop models for anomaly detection, fraud prevention, and responsible gaming interventions to maintain a safe and compliant environment.
• Design and evaluate A/B and multivariate tests for product features and marketing campaigns, ensuring statistically robust insights.
• Partner with Data Engineers to ensure model pipelines are production-ready, automated, and scalable across cloud environments.
• Visualize and present analytical findings to both technical and non-technical stakeholders, driving clear, data-informed decisions.
• Monitor and refine model performance, automate recurring analyses, and ensure data quality and reproducibility.

Requirements

• 3+ years of experience in a Data Scientist, Machine Learning Engineer, or advanced analytics role (ideally within iGaming, FinTech, or other consumer-facing digital products).
• Strong theoretical understanding of machine learning and statistical algorithms (classification, regression, clustering, optimization).
• Proven experience applying models to solve business problems (e.g., forecasting, segmentation, retention, player value optimization).
• Skilled in Python (Pandas, NumPy, Scikit-learn, PySpark) and SQL (advanced querying, ETL processes, performance optimization).