Gleantap

Compensation

Full Description

About Gleantap Gleantap is a customer engagement platform powering fitness, wellness, and service businesses. We’re evolving into an AI-native platform, where intelligent agents predict churn, upsell opportunities, and automate member engagement. We’re looking for a Python Data/ML Engineer who can bridge the gap between data engineering and applied machine learning, building pipelines, training models, and deploying them into production at scale. Responsibilities • Design and build data pipelines to transform raw events (visits, purchases, campaigns) into usable features. • Define and compute labels (e.g., churn, upsell, lead quality) from historical events. • Develop and train ML models (e.g., churn prediction, upsell propensity, lead scoring) using Python (scikit-learn, LightGBM, XGBoost). • Build real-time inference services to serve predictions into production systems. • Set up retraining and monitoring pipelines (Airflow, MLflow, or similar). • Collaborate with backend engineers to integrate model outputs into Gleantap workflows. • Ensure data quality, reproducibility, and compliance (HIPAA for healthcare customers). Requirements • 3–5+ years of experience in data engineering or applied ML. • Strong proficiency in Python, SQL, and one or more ML libraries (scikit-learn, LightGBM, XGBoost, PyTorch). • Experience with data pipelines (Airflow, dbt, or custom ETL). • Comfortable with event-driven systems (Kafka, Redis, ClickHouse or similar OLAP). • Understanding of ML lifecycle: training, serving, monitoring, retraining. • Ability to design time-based labels (avoiding data leakage). • Strong problem-solving skills and eagerness to work in a startup environment. Nice-to-Haves • MLOps tools (MLflow, BentoML, Ray Serve). • Experience with bandit algorithms, A/B testing, or uplift modeling. • Prior work with customer engagement, CRM, or subscription businesses.