Principal Data Scientist - Forecasting

Principal Data Scientist - Forecasting

Responsibilities:
- Lead development of advanced statistical, Bayesian, causal, and machine learning models to improve forecasting capabilities and quantify uncertainty for strategic decisions.
- Develop forecasting technology in partnership with Commercial, Operations, Supply Chain, Manufacturing, Finance, and Technology.
- Develop advanced forecasting models across near-, medium-, and long-term planning horizons.
- Use large, complex datasets and apply statistical modeling, causal inference, and analytics to generate business insights.
- Develop high-fidelity simulation and scenario evaluation capabilities for dynamics between patients, payers, and providers.
- Own the end-to-end modeling lifecycle (scoping, prototyping, data analysis, feature engineering, model development, deployment, monitoring, and explainability).
- Architect and develop self-service forecasting tools and platforms for near real-time leadership action.
- Collaborate cross-functionally to integrate forecasts into critical business workflows.
- Apply knowledge of Amgen systems/processes and industry trends to identify opportunities and risks.
- Research and evaluate emerging forecasting, data science, and AI tools/methodologies.
- Establish forecasting as an advanced, rigorous practice; upskill and mentor junior team members.

Basic Qualifications:
- Doctorate degree and 2 years of data science in enterprise environments OR
- Master’s degree and 4 years of data science in enterprise environments OR
- Bachelor’s degree and 6 years of data science in enterprise environments OR
- Associate’s degree and 10 years of data science in enterprise environments OR
- High school diploma/GED and 12 years of data science in enterprise environments.

Preferred Qualifications (skills/experience):
- 8+ years applying data science in enterprise environments with demonstrated principal-level influence.
- Deep expertise in time-series forecasting; probabilistic programming; Bayesian and predictive modeling; practical delivery of business-impacting models.
- Expert understanding of Python, SQL, and tools including scikit-learn, PyMC, PyTorch, TensorFlow, and other data science libraries.
- Strong communication/storytelling; ability to explain technical concepts and influence executive decisions.
- Exceptional stakeholder management; ability to drive alignment and communicate tradeoffs and metrics.
- Intellectual curiosity/self-starter; ability to build robust solutions from ambiguous problems.
- Experience building/scaling forecasting platforms for biotech/pharma; knowledge of healthcare commercial concepts (payer/provider dynamics, formulary access, coverage).
- Experience applying machine learning, operations research algorithms, and statistical modeling for retail/consumer goods/supply chain/manufacturing.
- Deep expertise in causal inference, Bayesian forecasting, and hierarchical/multi-level forecasting.
- Familiarity with ML Ops, CI/CD, and engineering best practices for scalable deployment and adoption.

Benefits:
- Total Rewards Plan (based on eligibility), including: retirement and savings plan (with generous company contributions); group medical, dental, and vision; life and disability insurance; flexible spending accounts; discretionary annual bonus (or sales incentive plan for field sales); stock-based long-term incentives; award-winning time-off; flexible remote/hybrid work where possible.

Application Instructions:
- Apply at careers.amgen.com.
- No application deadline; applications will be accepted until a sufficient number is received or a candidate is selected.