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Data Science Specialist

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Job Title: Data and Machine Learning Engineer

Gleantap is a customer engagement platform that powers fitness, wellness, and service businesses. We're evolving into an AI-native platform that predicts churn, upsell opportunities, and automates member engagement.

We are seeking a skilled data/ML engineer to join our team. This role will 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.