Senior Scientist, Machine Learning

$107,245 - $160,867/Yr

AstraZeneca - Gaithersburg, MD

Full-time - Senior

Gaithersburg, MD

1,001-5,000 employees

Chemical Manufacturing

About the position

The Senior Scientist specializing in Machine Learning at AstraZeneca is responsible for developing innovative machine learning methods to optimize biologics design and enhance drug delivery systems. This role involves collaboration with data and drug discovery scientists, designing experiments, and implementing state-of-the-art techniques in active learning. The position also emphasizes the importance of communication, training, and fostering external collaborations to advance the field of biologics engineering.

Responsibilities

• Collaborate with data and drug discovery scientists to develop machine learning methods that optimize biologics design.
• Identify opportunities in Biologics Engineering where machine learning can inform experiments.
• Recommend additional data and metadata to enhance model performance.
• Implement state-of-the-art techniques in an end-to-end active learning system in collaboration with machine learning experts in antibody design.
• Expand expertise in biologics engineering by learning from leading scientists.
• Build and maintain a high level of expertise with the informatics environment of the Biologics Engineering team, training bench scientists on data management and analysis processes.
• Foster strategic external collaborations, with opportunities to publish research in prestigious journals and conferences.
• Support the testing and experimental verification of data analysis tools and pipelines in collaboration with AstraZeneca partners.
• Effectively translate complex concepts for non-experts in internal and external scientific meetings.

Requirements

• PhD or 5 years of equivalent experience in a relevant computational field.
• Hands-on experience with machine learning frameworks and libraries.
• Experience with machine learning methods and their application in molecular biology.
• Proficiency in Python or Rust for analyzing experimental data.
• Strong communication skills and attention to detail; capable of building effective relationships with diverse individuals.
• Proven ability to work collaboratively as part of a team.
• Integrity and commitment to ethical practices.

Nice-to-haves

• Proven impact demonstrated through publications or contributions to code repositories.
• Experience with active learning, generative models, and Bayesian optimization.
• Understanding of data requirements for modeling biological data using machine learning techniques.
• Experience integrating domain knowledge from biology into machine learning methods.
• Familiarity with antibody discovery processes.

Benefits

• Competitive Total Reward program including a market driven base salary, bonus and long term incentive.
• Generous paid time off program.
• Comprehensive benefits package including medical, prescription drug, dental, and vision coverage.
• Qualified retirement program (401(k) plan).
• Paid vacation and holidays; paid leaves.