Data Scientist

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.

• →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.

• 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

• 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.