Data / ML Engineer

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 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.