Data Science Leader

2 days ago

Belgaum, Karnataka, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 25,00,000

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 are looking for a skilled professional to bridge the gap between data engineering and applied machine learning, building pipelines, training models, and deploying them into production at scale.

  • Design and build scalable data pipelines to transform raw events (visits, purchases, campaigns) into actionable insights.
  • Define and compute labels (e.g., churn, upsell, lead quality) from historical events to inform business decisions.
  • Develop and train machine learning 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, ensuring seamless integration with existing workflows.
  • Set up retraining and monitoring pipelines (Airflow, MLflow, or similar) to ensure data quality, reproducibility, and compliance.
  • Collaborate with backend engineers to integrate model outputs into business processes, driving informed decision-making.
  • Ensure data integrity, reliability, and security, adhering to industry standards and regulations (HIPAA for healthcare customers).

Requirements:

  • 3–5+ years of experience in data engineering or applied machine learning, with a strong track record of delivering high-quality results.
  • Strong proficiency in Python, SQL, and one or more machine learning libraries (scikit-learn, LightGBM, XGBoost, PyTorch), with hands-on experience in data modeling and analysis.
  • Experience with data pipelines (Airflow, dbt, or custom ETL), event-driven systems (Kafka, Redis, ClickHouse or similar OLAP), and time-based labels to avoid data leakage.
  • Understanding of the machine learning lifecycle: training, serving, monitoring, retraining, with a focus on iterative improvement and continuous learning.
  • Ability to design and implement efficient algorithms, with a strong problem-solving mindset and eagerness to tackle complex challenges.

Nice-to-Haves:

  • MLOps tools (MLflow, BentoML, Ray Serve), with experience in managing large-scale machine learning deployments.
  • Background in bandit algorithms, A/B testing, or uplift modeling, with a keen understanding of experimental design and statistical analysis.
  • Prior work with customer engagement, CRM, or subscription businesses, with a deep appreciation for the nuances of each industry.

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