We are looking for experienced ML Engineer in our Founder’s well funded startup in Sportbeting industry.
Main goal is to build the first production-grade ML platform layer for retention scoring: reliable data → reproducible features → stable model training → batch/near-real-time inference → monitoring and iteration.
You are the first ML Engineer. You own the technical foundation end-to-end.
Required Skills • Strong Python for ML and data processing (NumPy, pandas, scikit-learn). • Experience with gradient boosting models (CatBoost, XGBoost, LightGBM). • Experience building predictive models on tabular event data (e.g. churn, retention, risk). • Solid understanding of time-based features and leakage-safe modeling. • Strong analytical and statistical thinking. • Experience building ML training and batch inference pipelines.
Nice to Have
• Experience with behavioral feature engineering (rolling windows, aggregates). -Deep learning, pytorch/tf • Experience with clustering or user segmentation. • Experience deploying ML services (FastAPI, Docker). • Experience with ML lifecycle tools (MLflow or similar). • Experience with workflow orchestration tools (Airflow, Prefect, Dagster). • Experience with AWS or similar cloud environments.
Responsibilities • Implement churn / retention prediction models using behavioral player data. • Build time-based features (rolling windows, cumulative metrics, decay features). • Train, validate, and iteratively improve models under guidance of the ML Lead. • Develop reproducible training and batch scoring pipelines. • Analyze model performance and improve feature sets. • Support deployment of models into production scoring services.
Tech Stack
Python, NumPy, pandas, scikit-learn CatBoost / XGBoost SQL FastAPI, Docker MLflow (or equivalent) AWS or similar cloud environment
