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Strong employer brand but senior, specialized ML forecasting role reduces applicant density.
Core ML engineering skills are transferable, but forecasting and regulated biotech context increase domain specificity.
Explicit 8+ years and mandatory production ML, forecasting, and MLOps experience enforce strict candidate filters.
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Design, build, deploy, and maintain scalable ML systems and forecasting pipelines supporting short-, medium-, and long-term demand forecasting.
Productionize advanced forecasting models (statistical, Bayesian, ML) including training, validation, deployment, and lifecycle management.
Develop robust MLOps capabilities (versioning, CI/CD, monitoring, retraining) and collaborate with data scientists and stakeholders to operationalize forecasting solutions.
8+ years experience in machine learning engineering or related field with proven production ML system delivery.
Proven expertise in end-to-end ML pipeline development and deployment for forecasting or predictive modeling.
Strong programming skills in Python and SQL; experience with ML libraries (scikit-learn, PyTorch, TensorFlow) and ML workflow tools.
Experience with cloud platforms, distributed data processing, containerization, and software engineering fundamentals.
Experienced in operationalizing complex forecasting models into reliable production-grade services.
Comfortable leading end-to-end ML engineering lifecycle including MLOps and software engineering best practices.
Familiar with business-driven, multi-horizon forecasting use cases, preferably in biotech/pharma or related regulated industries.