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.