Senior Machine Learning Engineer - Forecasting

Career Category

Information Systems

Job Description

Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Senior Machine Learning Engineer - Forecasting

What You Will Do

We are seeking a Senior Machine Learning Engineer, Forecasting to join the Forecasting team within the AI & Data organization. This role will design, build, deploy, and maintain scalable machine learning systems that power forecasting capabilities and uncertainty-aware decision support across the company.

This senior member of the team will work cross-functionally to translate advanced forecasting methods into reliable, production-grade solutions that support critical business processes and help Amgen deliver on its “every patient, every time” mandate. The role is particularly well suited to a strong engineer who is excited about building robust ML infrastructure, productionizing state-of-the-art forecasting models, and enabling decision-support solutions that inform multi-horizon planning and business decision-making.

Key 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.

Basic Qualifications

• 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 

  Preferred Qualifications

  • 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 engin