Senior Data & Machine Learning Engineer (B2B)

We are a small Danish company building a sports betting aggregator platform. Billions of bets flow through our system each year from some of the most sophisticated professional bettors in the world.We are hiring a self-driven engineer to design, build, and own a real-time service that evaluates incoming bets and manages our risk. The goal is to decide in under 100 ms whether a bet is statistically likely to win or lose, using both extensive historical data and live signals. When the model identifies favorable opportunities, we can take a small percentage exposure on the bet; therefore, rigorous, real-time risk management is essential.The sports betting market evolves quickly. Rules change, bettor behaviour shifts, bookmaker prices update continuously, and settlement criteria can vary. Your systems must detect concept drift, learn from new patterns, and improve continuously.Our backend is C# on Azure, with data in SQL Server and Azure Cosmos DB. We are otherwise pragmatic about tools. We expect you to use a mix of machine learning, statistics, and streaming systems to reach the latency and decision quality targets.Your ResponsibilitiesDesign and implement a low-latency scoring service for real-time bet evaluation.Build streaming data pipelines that join live odds, market structure, bettor activity, and match telemetry (shots on goal, possession, and similar feed events).Analyze millions of historical bets to inform real-time decision making. Develop models and statistical decision rules for win probability, expected value, and uncertainty.Create a risk engine that enforces exposure limits, stop-loss and take-profit rules, and capital allocation per league, market, and counterparty.Stand up robust machine learning operations: feature computation and storage, validation, automated retraining, and observability for latency, accuracy, and PnL impact.Work closely with backend engineers to integrate with our C# services on Azure, including authentication, monitoring, and incident response.Work closely with sports betting experts for feature engineering.Establish auditability: model lineage, reproducible backtests, and decision logs for compliance and post-trade analysis.Own the problem end-to-end. You will be the primary driver of this domain.Your ProfileStrong experience building production ML or decisioning systems with tight latency constraints or high throughput.Deep grasp of probability and statistics.Proven machine learning operational skills: models, feature pipelines, monitoring, automated retraining, and back-testing.Proficiency with relational SQL. Experience with Azure and Cosmos DB is useful.Practical self-driven mindset. You ship reliable systems and measure results with clear metrics.Nice to haveSports betting or exchange experience, or familiarity with market-making and risk controls.Experience with C# or similar.Our stack todayC# backend on AzureData in SQL Server and Azure Cosmos DBReal-time ingestion from external APIs