Senior Machine Learning Engineer - Forecasting
Amgen
4 hours ago
Remote friendly (United States)
United States
IT
Senior Machine Learning Engineer - Forecasting
Responsibilities:
- Design, build, and maintain scalable machine learning systems and forecasting pipelines for near-, medium-, and long-term demand forecasting.
- Productionize advanced statistical, Bayesian, and ML forecasting models (training, validation, deployment, lifecycle management).
- Build and optimize data pipelines, feature engineering workflows, and batch and real-time inference systems.
- Own the end-to-end ML engineering lifecycle (solution design, prototyping, integration, testing, deployment, monitoring, observability, continuous improvement).
- Develop MLOps capabilities including model versioning, CI/CD, automated retraining, performance monitoring, drift detection, and rollback strategies.
- Partner with data scientists and business stakeholders to operationalize forecasting, simulation, and scenario-analysis capabilities.
- Establish and promote software engineering best practices (code quality, documentation, reproducibility, reliability).
- Research and evaluate emerging ML/forecasting/AI tools and methodologies for business applications.
Basic Qualifications:
- Doctorate degree OR
- Master’s degree and 2 years of applying data science in enterprise environments OR
- Bachelor’s degree and 4 years of applying data science in enterprise environments OR
- Associate’s degree and 8 years of applying data science in enterprise environments OR
- High school diploma/GED and 10 years of applying data science in enterprise environments
Preferred Qualifications:
- 6+ years experience in ML engineering/software engineering (or related), with deployed production ML systems delivering business value.
- End-to-end ML pipelines and production systems for forecasting/predictive modeling.
- Model serving; operationalizing probabilistic/Bayesian/predictive models in production.
- Strong Python and SQL; experience with scikit-learn, PyTorch, TensorFlow, and ML orchestration/workflow tools.
- Cloud platforms, distributed data processing, containerization, and ML deployment patterns.
- Software engineering fundamentals (system design, testing, performance optimization, maintainability).
- Collaboration/communication across technical and non-technical teams; self-starter able to build scalable solutions from ambiguity.
- Biotech/pharma forecasting experience and healthcare commercial concepts (payer/provider dynamics, formulary access, coverage).
- Experience translating advanced models into reliable production services.
- Experience in retail/consumer goods/supply chain/manufacturing forecasting applications.
- Familiarity with model monitoring, explainability, and governance in regulated/high-impact environments.
Application Instructions:
- Apply now via careers.amgen.com.
Responsibilities:
- Design, build, and maintain scalable machine learning systems and forecasting pipelines for near-, medium-, and long-term demand forecasting.
- Productionize advanced statistical, Bayesian, and ML forecasting models (training, validation, deployment, lifecycle management).
- Build and optimize data pipelines, feature engineering workflows, and batch and real-time inference systems.
- Own the end-to-end ML engineering lifecycle (solution design, prototyping, integration, testing, deployment, monitoring, observability, continuous improvement).
- Develop MLOps capabilities including model versioning, CI/CD, automated retraining, performance monitoring, drift detection, and rollback strategies.
- Partner with data scientists and business stakeholders to operationalize forecasting, simulation, and scenario-analysis capabilities.
- Establish and promote software engineering best practices (code quality, documentation, reproducibility, reliability).
- Research and evaluate emerging ML/forecasting/AI tools and methodologies for business applications.
Basic Qualifications:
- Doctorate degree OR
- Master’s degree and 2 years of applying data science in enterprise environments OR
- Bachelor’s degree and 4 years of applying data science in enterprise environments OR
- Associate’s degree and 8 years of applying data science in enterprise environments OR
- High school diploma/GED and 10 years of applying data science in enterprise environments
Preferred Qualifications:
- 6+ years experience in ML engineering/software engineering (or related), with deployed production ML systems delivering business value.
- End-to-end ML pipelines and production systems for forecasting/predictive modeling.
- Model serving; operationalizing probabilistic/Bayesian/predictive models in production.
- Strong Python and SQL; experience with scikit-learn, PyTorch, TensorFlow, and ML orchestration/workflow tools.
- Cloud platforms, distributed data processing, containerization, and ML deployment patterns.
- Software engineering fundamentals (system design, testing, performance optimization, maintainability).
- Collaboration/communication across technical and non-technical teams; self-starter able to build scalable solutions from ambiguity.
- Biotech/pharma forecasting experience and healthcare commercial concepts (payer/provider dynamics, formulary access, coverage).
- Experience translating advanced models into reliable production services.
- Experience in retail/consumer goods/supply chain/manufacturing forecasting applications.
- Familiarity with model monitoring, explainability, and governance in regulated/high-impact environments.
Application Instructions:
- Apply now via careers.amgen.com.