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About our company
Our organization is a customer engagement platform that powers businesses in the fitness, wellness, and service sectors. We are evolving into an AI-native platform where intelligent agents predict customer churn, identify upsell opportunities, and automate member engagement.
We are seeking a skilled professional who can bridge the gap between data engineering and applied machine learning. This role involves building pipelines to transform raw events into usable features, defining labels from historical events, developing and training machine learning models, and deploying them at scale.
Key Responsibilities:- Design and implement data pipelines to process raw events (visits, purchases, campaigns) and extract relevant features.
- Develop and train machine learning models using Python libraries (scikit-learn, LightGBM, XGBoost) to predict customer behavior.
- Build real-time inference services to serve predictions into production systems.
- Collaborate with backend engineers to integrate model outputs into workflows.
- 3–5+ years of experience in data engineering or applied ML.
- Strong proficiency in Python, SQL, and one or more ML libraries.
- Experience with data pipelines (Airflow, dbt, or custom ETL).
- Understanding of machine learning lifecycle: training, serving, monitoring, retraining.
