Senior Machine Learning Engineer - Forecasting

Amgen is a biotechnology company dedicated to serving patients living with serious illnesses. They are seeking a Senior Machine Learning Engineer for the Forecasting team to design, build, and maintain scalable machine learning systems that enhance forecasting capabilities and decision support across the company.

Responsibilities:

• Design, build, and maintain scalable machine learning systems and forecasting pipelines to support demand forecasting across near-, medium-, and long-term planning horizons
• Productionize advanced statistical, Bayesian, and machine learning forecasting models, including training, validation, deployment, and lifecycle management
• Build and optimize data pipelines, feature engineering workflows, and batch and real-time inference systems using large, complex datasets
• Own the end-to-end ML engineering lifecycle, including solution design, prototyping, model integration, testing, deployment, monitoring, observability, and continuous improvement
• Develop robust MLOps capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, drift detection, and rollback strategies
• Partner closely with data scientists and business stakeholders to operationalize forecasting, simulation, and scenario-analysis capabilities that support strategic decision-making
• Establish and promote software engineering best practices, including code quality, documentation, reproducibility, and system reliability
• Research and evaluate emerging tools, platforms, and methodologies in machine learning engineering, forecasting, and AI for potential application to business problems

Requirements:

• Doctorate degree OR
• Master's degree and 2 years of applying data science in enterprise environments experience OR
• Bachelor's degree and 4 years of applying data science in enterprise environments experience OR
• Associate's degree and 8 years of applying data science in enterprise environments experience OR
• High school diploma / GED and 10 years of applying data science in enterprise environments experience
• 6+ years of experience in machine learning engineering, software engineering, or a related field, with a demonstrated track record of deploying production ML systems that deliver business value
• Strong experience building and maintaining end-to-end ML pipelines and production systems for forecasting or other predictive modeling use cases
• Expertise in model serving, and operationalizing probabilistic, Bayesian, or predictive models in production environments
• Strong programming skills in Python and SQL, with experience using tools such as scikit-learn, PyTorch, TensorFlow, and orchestration or workflow tools for ML pipelines
• Experience with cloud platforms, distributed data processing, containerization, and ML deployment patterns
• Strong understanding of software engineering fundamentals, including system design, testing, performance optimization, and maintainability
• Strong collaboration and communication skills, with the ability to work effectively across technical and non-technical teams
• An intellectually curious self-starter who can take ambiguous problems and build scalable solutions from the ground up
• Experience building and deploying forecasting models for biotech/pharma use cases with knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, and coverage
• Experience partnering closely with data scientists to translate advanced statistical or machine learning models into reliable production services
• Experience leveraging machine learning and forecasting systems in retail, consumer goods, supply chain, or manufacturing applications
• Familiarity with model monitoring, explainability, and governance requirements in regulated or high-impact business environments