Principal Data Scientist

HOW MIGHT YOU DEFY IMAGINATION?

You’ve earned your degree. How will you use that achievement to reach your goals? Do more with the knowledge you’ve worked hard to acquire and the passion you already have. At Amgen, our shared mission—to serve patients—drives all that we do. It is key to our becoming one of the world’s leading biotechnology companies, reaching over 10 million patients worldwide. Become the professional you are meant to be in this meaningful role.

Principal data scientist - Advance statistical modeling and ML expert

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What you will do

Let’s do this. Let’s change the world. In this vital role you will be responsible for designing, developing, and applying advanced statistical models, including Generalized Linear, mixed effect models and Bayesian methods( modeling, inference and compute purposes), to address complex research questions. The role will also involve some understanding of causal inference techniques to derive meaningful insights from observational data and randomized trials. The ideal candidate will have a strong foundation in statistics, computational skills, and a deep understanding of how to apply statistical methods to real-world problems. Some acquaintance with advanced LLMs techniques will also be a plus

Key Responsibilities:

• Develop and apply Generalized Linear Mixed Models (GLMM) for various research problems.
• Design and implement Bayesian models ( i.e Basic Bayesian inference, Bayesian hierarchical models, computation bayesian methods -MCMC, Variational inference etc) for predictive analytics tasks and improve decision-making.
• Perform causal inference analyses using techniques such as propensity score matching, instrumental variable analysis, and difference-in-differences methods.
• Collaborate with cross-functional teams including data scientists, epidemiologists, and clinical researchers to design studies and analyze data.
• Communicate complex statistical methods and results to non-statistical audiences clearly and effectively.
• Provide mentorship and guidance to junior statisticians and data scientists.
• Stay current with advancements in statistical methods, software, and best practices.

Basic Qualifications:

• Doctorate degree OR
• Master’s degree and 2 years of statistical modeling experience OR
• Bachelor’s degree and 4 years of  statistical modeling experience OR
• Associate’s degree and 8 years of  statistical modeling experience OR
• High school diploma / GED and 10 years of statistical modeling experience

Preferred Qualifications

• PhD or Master's degree in Statistics, Biostatistics, Data Science, or a related field.
• 5+ years of experience in applying GLMM, Bayesian modeling, and causal inference in a research or industry setting.
• Proficiency in statistical programming languages such as R, Python, or SAS.
• Good understanding of causal inference methods and their application to observational data and randomized control trials.
• Excellent problem-solving skills and ability to translate complex statistical concepts into actionable insights.
• Strong communication skills and the ability to work effectively in a team environment.