Senior ML Engineer

As a Senior ML Engineer, you will work on retention and churn prediction models built on large-scale behavioral datasets.
You will focus on feature engineering, model development, and building reliable training and batch inference pipelines that transform player activity data into production-ready predictive systems.
This role combines strong analytical thinking with practical ML engineering skills and close collaboration with the ML Lead.

Responsibilities

• Build and improve churn and retention prediction models using player behavioral data
• Design time-based features (rolling windows, aggregates, decay metrics)
• Train, validate, and iteratively improve gradient boosting models
• Develop reproducible training and batch scoring pipelines
• Analyze model performance and refine feature sets

• Support deployment of models into production scoring services

   

Requirements

• 4+ years of experience in ML Engineering / MLOps with production systems.
• 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 (churn, retention, risk)
• Strong understanding of time-based feature engineering and leakage-safe modeling
• Experience building ML training and batch inference pipelines
  Strong analytical and statistical thinking

Would Be a Plus

• Experience with behavioral feature engineering (rolling windows, aggregates)
• 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
• Experience with deep learning frameworks (PyTorch / TensorFlow)

Tech Stack

• Python, NumPy, pandas, scikit-learn
• CatBoost / XGBoost
• SQL
• FastAPI, Docker
• MLflow (or similar)
• AWS or similar cloud environment

What We Offer

• Full ownership of a greenfield ML platform.
• Direct impact on revenue-driving retention systems.
• High level of autonomy in technical decisions.
• Competitive compensation.
• Flexible working format.
• Opportunity to shape the ML engineering foundation of the product.