Senior Machine Learning Engineer

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 Machine Learning Engineer

What you will do

Let’s do this. Let’s change the world. In this vital role you will be part of the technical/engineering team, develop data flow pipelines to extract, transform, and load data from various data sources in various data format to enterprise data lake and data warehouse system in three regions in AWS, provide data analytics and predictive analysis to business users. We look for people who can work in a team, able to mentor junior engineers, is curious to learn and able to develop data engineering and machine learning engineering solution in a fast-moving environment.

• Be a key team member assisting in design and development of the data pipeline for Global Data and Analytics team, such as data cleaning and transformation.

• Able to explore, understand various datasets used in biotech/pharma commercial data analytics; Able to create informative and appealing data visualizations.

• Work with Data Scientist to perform data cleaning, statistical analysis, feature engineering; Develop pipeline for model selection, training, and evaluation.

• Understand experimental design and conducting A/B tests for data-driven decision-making.

• Ensure consistent feature engineering between training and model serving.

• Automate model deployment, monitoring, model retrain process.

• Adhere to best practices for coding, testing and designing reusable code/component.

• Flexible to work on data engineering or machine learning projects based on current product backlog within the team.

• Able to explore new tools, technologies that will help to improve ETL platform performance and machine learning operations.

• Able to work effectively in cross-functional teams and collaborate with data engineers, analysts, and business stakeholders.

• Strong communication skills to convey insights and findings to non-technical stakeholders.

• Able to stay updated with the latest trends and advancements in data science/machine learning technologies.

• Mentor junior data/machine learning engineer

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The ML professional we seek will have these qualifications.

Basic Qualifications:

Doctorate degree

OR

Master’s degree and 2 years of Data Science/Machine Learning experience

Or

Bachelor’s degree and 4 years of Data Science/Machine Learning experience

Or

Associate’s degree and 8 years of Data Science/Machine Learning experience

Or

High school diploma / GED and 10 years of Data Science/Machine Learning experience

Preferred Qualifications:

• Strong programming skills in Python or R, library and packages related to data manipulation, statistical analysis, chart/plot, and machine learning algorithms and framework.

• Outstanding analytical and problem-solving skills; Ability to learn quickly; experience in model selection, training, and evaluation.

• Familiar with PySpark dataframe and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries

• Familiar with Machine Learning life cycle, be able to implement feature store, MLflow, model registry, model deployment, model serving, model monitoring

• Proficiency in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification

• Experience with data modeling for both OLAP and OLTP databases, hands-on experience with SQL, especially SparkSQL performance tuning

• Experience with software