Data Scientist

Data Scientist

Crypto-native iGaming operator · Hybrid · Competitive base + strong growth potential

THE OPPORTUNITY

We're one of the fastest-growing crypto-native casinos in the market, scaling across multiple licences, brands, and player bases. Data science here isn't a support function running dashboards for someone else's decisions — it's a first-class discipline that shapes how the product grows, how players are engaged, and how every commercial call gets made.

You'll work at the sharp edge of a modern data stack: real-time streaming, live experimentation, cloud-native ML pipelines, and models that get deployed in days, not quarters. Every model you ship shows up in a live product, a live campaign, or a live decision the next morning. Small team, senior audience, no six-month roadmap wait.

WHAT YOU'LL ACTUALLY DO

•Build models end-to-end — feature engineering, training, validation, deployment, and monitoring in production

•Design player-behaviour, segmentation, and lifetime-value models that shape CRM, VIP, and retention strategy

•Own recommendation and personalisation models — the right game, the right offer, the right moment for the right player

•Model marketing efficiency: attribution, uplift, incrementality, and paid-channel ROI across acquisition and reactivation

•Design and run the experimentation framework — A/B, multi-armed bandits, causal inference, and honest post-launch readouts

•Partner with Product on data-informed roadmap decisions — pricing, mechanics, bonus economics, and new-market launches

•Collaborate with engineering on productionisation — model registry, feature store, CI/CD for models, drift monitoring, clean handovers

•Bring the story — visualisations and readouts that make model behaviour legible to non-technical leadership

WHO YOU ARE

•A relevant degree in Data Science, Mathematics, Statistics, Computer Science, Physics, or a closely related quantitative discipline — this is a non-negotiable for the role

•3-6 years of hands-on data-science experience, ideally with meaningful exposure to consumer product, marketplace, gaming, fintech, or subscription businesses

•Strong Python and SQL — you can go from a fresh dataset to a validated model without waiting on anyone else

•Deep familiarity with the modern modelling toolkit: gradient boosting, embedding methods, recommender systems, causal inference, and sensible use of deep learning where it earns its keep

•Genuine understanding of experimental design, statistical significance, and the operational cost of a bad decision on a real user

•Comfort with production data science — model registry, feature store, CI/CD for models, monitoring, and clean handovers to engineering

•Scale-up-ready: shifting priorities, small team, direct exposure to leadership, no hiding behind a Jira column

NICE TO HAVE (NOT REQUIRED)

•MSc or PhD in a quantitative discipline

•Real-time inference systems and low-latency feature pipelines

•Recommender systems, graph learning, or sequence models applied to consumer behaviour

•Prior iGaming, sportsbook, or live-casino modelling experience

•Exposure to causal-inference toolkits, uplift modelling, or Bayesian methods in production

•Cloud-native ML stack (AWS/GCP/Azure), dbt, Airflow, or similar orchestration

WHERE THIS ROLE GOES

This isn't a static seat. Strong performers move into senior data scientist, tech lead, and specialist modelling roles as the business scales into new markets and products. The team already has former ICs now leading their own workstreams — ambition and curiosity get noticed here.