Job 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.
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