Lead end-to-end development of predictive ML models for user engagement and cardiovascular risk
Research and implement ML techniques across supervised/unsupervised learning, causal inference
Deploy production-grade Python code with maintainability and scalability in mind.
Collaborate with product managers, data engineers, and software engineers to translate questions
Contribute to MLOps: model serving, versioning, evaluation pipelines, and monitoring.
Design and interpret A/B tests to measure impact of models and interventions.

🎯 Requirements

5+ years of hands-on ML production experience.
Bachelor's degree in a quantitative field.
Strong foundations in statistics, probability, Bayesian methods, and experimental design, with
Proficiency in Python (production-grade, testing, reproducibility).
Experience with AI coding assistants in development workflow.
Expertise with ML frameworks (PyTorch, scikit-learn, XGBoost, LightGBM).

🎁 Benefits

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