CAREER CATEGORY

Engineering

JOB DESCRIPTION

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 team-oriented
problem solver who is excited about applying forecasting methods, complex
datasets, and up-to-date 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 recent 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
2+ years of experience applying data science, analytics, or statistical
modeling in industry, research, or enterprise environments.
Experience with time-series forecasting, predictive mo

📌 Associate Data Scientist Hyderabad
🏢 AMGEN
📍 Hyderabad