About the role
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 and ship AI solutions based on LLMs, such as AI agents, retrieval-augmented generation (RAG) and automated analysis and reporting, that teams across the business use every day. • Help shape how Betty adopts AI: find high-value use cases, evaluate models and tools, and set up checks that AI outputs are accurate, reliable and cost-effective. • Partner with the marketing and user acquisition teams on channel and campaign performance, using metrics such as CAC, ROAS, CPA, first-time depositors (FTDs), conversion rates and predicted LTV. • Build marketing models such as predicted LTV for newly acquired players, attribution, marketing mix modeling, incrementality measurement and budget allocation across channels. • Work with analysts, marketing, product and business stakeholders to turn business questions into clear requirements, success metrics and use cases. • Work with large event-level data in Redshift, and combine and clean data from internal and external sources such as player activity, transactions, payments, CRM and marketing platforms. • Build statistical and machine learning models for problems such as player lifetime value, churn and retention, segmentation, forecasting and driver analysis. • Form and test hypotheses about player and commercial behavior, and turn the answers into clear recommendations, including how they affect our players. • Find opportunities and inefficiencies across player segments, games, providers, payment flows and channels, and run deep-dives that support product launches and marketing campaigns. • Support experimentation and A/B testing: define success metrics, analyze results and know when a result is real. • Take models from prototype to production and own them end to end, from batch jobs on daily, weekly and monthly schedules to real-time inference where speed matters. • Build and run real-time pipelines that stream player events (for example with Kafka or Flink), compute features on live data and serve predictions with low latency, such as real-time churn and retention signals. • Set up the MLOps foundations: CI/CD for models, model registry and versioning, a feature store, automated retraining, and monitoring for drift, data quality and model performance. • Present results through dashboards and clear recommendations to any audience, up to C-level. • Help other teams use data and AI on their own, through self-service analytics and shared environments where they can safely deploy their own AI tools. • Work within the rules of a regulated market, including responsible gambling and data privacy requirements.
Requirements • 3+ years of experience as a Data Scientist/ML Engineer. • Strong knowledge of statistics and machine learning methods, such as predictive modeling, classification, clustering, regression, and optimization. • Advanced Python with strong software engineering practices (testing, code review, Git), and advanced SQL with Redshift or a comparable cloud data warehouse. • Proven experience deploying and running ML models in production, including CI/CD, containers (Docker), model versioning, automated retraining and monitoring for drift and performance. • Hands-on experience building solutions with LLMs and generative AI, such as retrieval-augmented generation (RAG), AI agents and tool use, prompt engineering and NLP. • Experience working with LLM APIs and frameworks, and with evaluating AI outputs for accuracy, reliability, and cost. • Good judgment on where AI adds value and where a simpler method works better, and you keep up with new models and tools. • Proven experience applying models to business problems such as forecasting, segmentation, lifetime value, retention, churn, or engagement. • Experience with large datasets (100M+ rows and many variables). • The ability to work with messy, incomplete, and changing data and still produce reliable results. • Clear communication: you can explain complex work to people who don't want the detail, and manage expectations honestly. • Comfortable working in a fast-paced environment. • Fluent English, written and spoken.
Nice to have: • Strongly preferred: experience with marketing data and its terminology, such as user acquisition, CAC, ROAS, FTDs, attribution, marketing mix modeling, incrementality, predicted LTV, and campaign and creative performance. • Online casino experience from any part of the business, or experience in iGaming, sports betting, gaming, fintech or another high-volume consumer business. • Hands-on Tableau or a comparable BI tool. • Experience with streaming data and real-time ML: event streaming (Kafka, Flink or similar), feature computation on live data and low-latency model serving through APIs. • Hands-on experience with MLOps and orchestration tools such as MLflow, Airflow, Kubernetes, Terraform or a feature store. • Experience working with APIs and third-party data sources. • Experience running LLM applications in production, including vector databases, agent frameworks, output evaluation and cost monitoring.
What we offer • Competitive salary • Performance-based bonus • Career and skills development opportunities • Fun and collaborative team environment • New modern office space
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