Python Engineer (Data / ML)
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.
Apply Now
Apply Now
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.
Apply Now
Apply Now