Data Scientist

Job description

Career Category Engineering

ABOUT THE ROLE

We are seeking a Data Scientist, Forecasting to join the Forecasting team within the AI & Data organization. This role will support the development of statistical, Bayesian, causal, and machine learning models that enhance forecasting capabilities and quantify uncertainty to guide decision-making across the company. This role will work cross-functionally to help build forecasting solutions that support critical business processes and contribute to Amgen’s “every patient, every time” mandate.

The position is well suited to a curious and collaborative problem solver who is excited about applying forecasting methods, complex datasets, and modern analytical tools to build decision-support solutions that inform planning and business decision-making.

Key Responsibilities

Develop and refine statistical, Bayesian, and machine learning models to support demand forecasting across near-, medium-, and long-term planning horizons. Analyze large, complex datasets using statistical modeling, forecasting, and analytics techniques to generate insights that support business decision-making. Support the development of simulation and scenario-analysis capabilities to better understand dynamics among patients, payers, providers, and market conditions.

Contribute across the modeling lifecycle, including business problem framing, exploratory data analysis, feature engineering, model development, validation, deployment support, monitoring, and explainability. Collaborate with senior team members to evaluate and apply new tools and methodologies in forecasting, data science, and AI to business problems. Communicate analytical findings and model results clearly to technical and non-technical stakeholders.

Basic Qualifications

4+ years of experience applying data science, analytics, or statistical modeling in industry, research, or enterprise environments. Experience with time-series forecasting, predictive modeling, or related quantitative methods. Proficiency in Python and SQL, with experience using tools such as scikit-learn, PyMC, PyTorch, TensorFlow, or similar data science libraries. Solid analytical and statistical foundation, with the ability to work with messy and complex datasets to generate actionable insights.

Familiarity with model development workflows, including data preparation, feature engineering, evaluation, and interpretation. Strong communication skills, with the ability to explain technical concepts and results to a range of stakeholders. Strong collaboration skills and ability to work effectively in cross-functional environments. Intellectual curiosity and a willingness to learn, take on ambiguous problems, and grow in a fast-paced environment

Preferred Qualifications

Experience applying machine learning, statistical modeling, or decision science methods in retail, consumer goods, supply chain, manufacturing, or similar settings. Exposure to causal inference, probabilistic modeling, or scenario analysis is a plus. .