Senior Data Scientist

Career Category

Information Systems

Job Description

Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Senior Data Scientist

What you will do

We are seeking a Senior Data Scientist to join the Forecasting team, within the AI & Data organization. This role will develop advanced statistical, Bayesian, causal, and machine learning models that improve forecasting capabilities and quantify uncertainty to guide strategic decision-making across the company.

This senior member of the team will work cross-functionally to build forecasting solutions that support critical business processes and help Amgen deliver on its “every patient, every time” mandate. The role is particularly well suited to a creative problem solver who is excited about utlizing state-of-the-art forecasting methods, complex high-dimensional data sources, and modern analytical tooling to build decision-support solutions that inform multi-horizon planning and business decision-making.

Key Responsibilities

• Develop advanced statistical, Bayesian, and machine learning models to forecast demand across multiple horizons, including near-, medium-, and long-term planning horizons.

• Work with large, complex datasets, leveraging state-of-the-art techniques in statistical modeling, causal inference, and analytics to generate insights that support strategic decision-making across the business.

• Develop simulation and scenario-analysis capabilities to better understand the complex dynamics among patients, payers, providers, and market conditions.

• Execute across the end-to-end modeling lifecycle, including scoping, prototyping, data analysis, feature engineering, model development, deployment and, monitoring as well as explainability.

• Collaborate with cross functional teams in Commercial, Operation, Finance and Technology to ensure forecasts are well integrated into critical business workflows.

• Research and evaluate emerging tools and methodologies in forecasting, data science and AI for potential application to business probelms.

Basic Qualifications

• Doctorate degree OR

• Master’s degree and 2 years of applying data science in enterprise environments experience OR

• Bachelor’s degree and 4 years of applying data science in enterprise environments experience OR

• Associate’s degree and 8 years of applying data science in enterprise environments experience OR 

• High school diploma / GED and 10 years of applying data science in enterprise environments experience 

Preferred Qualifications

• 6+ years of experience applying data science in enterprise environments with demonstrated track record of delivering business value.

• Expertise in time-series forecasts, probabilistic programming, Bayesian and predictive modeling.

• Strong understanding of Python, SQL and tools such as scikit-learn, PyMC, Pytorch, Tensorflow and other data science libraries.

• Strong analytical and statistical intuition, with ability to generate novel insights from messy, complex datasets using techniques such as latent variable modeling, high dimensional clustering, and causal inference.

• Strong communication and story-telling skills, with demonstrated ability to translate technical concepts to non-technical stakeholders.

• Strong collaboration skills and ability to work effectively cross functionally.

• An intellectually curious self-starter who can take ambiguous problems and build solutions from the ground up.

• Experience building and developing forecasting models for biotech/pharma use cases with knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, and c