About this 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
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We are honest: we value honesty in all aspects.
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Bring the Olives: we offer premium customer experience.
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Think Big: we believe in always striving for more.
Key responsibilities:
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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.
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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.
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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.
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Build marketing models such as predicted LTV for newly acquired players, attribution, marketing mix modeling, incrementality measurement and budget allocation across channels.
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Work with analysts, marketing, product and business stakeholders to turn business questions into clear requirements, success metrics and use cases.
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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.
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Build statistical and machine learning models for problems such as player lifetime value, churn and retention, segmentation, forecasting and driver analysis.
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Form and test hypotheses about player and commercial behavior, and turn the answers into clear recommendations, including how they affect our players.
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Find opportunities and inefficiencies across player segments, games, providers, payment flows and channels, and run deep-dives that support product launches and marketing campaigns.
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Support experimentation and A/B testing: define success metrics, analyze results and know when a result is real.
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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.
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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.
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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.
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Present results through dashboards and clear recommendations to any audience, up to C-level.
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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.
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Work within the rules of a regulated market, including responsible gambling and data privacy requirements.
Requirements
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3+ years of experience as a Data Scientist/ML Engineer.
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Strong knowledge of statistics and machine learning methods, such as predictive modeling, classification, clustering, regression, and optimization.
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Advanced Python with strong software engineering practices (testing, code review, Git), and advanced SQL with Redshift or a comparable cloud data warehouse.
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Proven experience deploying and running ML models in production, including CI/CD, containers (Docker), model versioning, automated retraining and monitoring for drift and performance.
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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.
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Experience working with LLM APIs and frameworks, and with evaluating AI outputs for accuracy, reliability, and cost.
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Good judgment on where AI adds value and where a simpler method works better, and you keep up with new models and tools.
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Proven experience applying models to business problems such as forecasting, segmentation, lifetime value, retention, churn, or engagement.
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Experience with large datasets (100M+ rows and many variables).
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The ability to work with messy, incomplete, and changing data and still produce reliable results.
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Clear communication: you can explain complex work to people who don't want the detail, and manage expectations honestly.
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Comfortable working in a fast-paced environment.
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Fluent English, written and spoken.
Nice to have:
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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.
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Online casino experience from any part of the business, or experience in iGaming, sports betting, gaming, fintech or another high-volume consumer business.
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Hands-on Tableau or a comparable BI tool.
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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.
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Hands-on experience with MLOps and orchestration tools such as MLflow, Airflow, Kubernetes, Terraform or a feature store.
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Experience working with APIs and third-party data sources.
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Experience running LLM applications in production, including vector databases, agent frameworks, output evaluation and cost monitoring.
What we offer
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Competitive salary
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Performance-based bonus
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Career and skills development opportunities
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Fun and collaborative team environment
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New modern office space