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Tier-1 employer plus desirable ML specialization results in medium competition.
Core ML engineering skills are transferable, but forecasting and regulated biotech domain knowledge limit portability.
Explicit 8+ years, production ML, forecasting, and MLOps requirements increase shortlisting strictness.
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Design, build, deploy, and maintain scalable machine learning systems and forecasting pipelines for demand forecasting across multiple planning horizons.
Productionize advanced statistical, Bayesian, and machine learning forecasting models including lifecycle management and MLOps capabilities like CI/CD, monitoring, and drift detection.
Collaborate cross-functionally to operationalize forecasting and decision-support solutions enabling multi-horizon planning and strategic business decisions.
8+ years experience in machine learning engineering or related field with production ML system deployment.
Strong programming skills in Python and SQL; experience with ML frameworks like scikit-learn, PyTorch, TensorFlow, and ML pipeline orchestration tools.
Experience with cloud platforms, distributed data processing, containerization, and ML production deployment patterns.
Strong software engineering fundamentals: system design, testing, performance optimization, maintainability.
Experienced in building and operationalizing end-to-end ML pipelines specifically for forecasting or predictive modeling.
Comfortable working in cross-functional teams with data scientists and business stakeholders to translate complex models into reliable production services.
Prior experience or understanding of biotech/pharma domain and healthcare commercial concepts is preferred but not mandatory.