Senior Data Scientist

at  Amgen

HOW MIGHT YOU DEFY IMAGINATION?

You’ve worked hard to become the professional you are today and are now ready to take the next step in your career. How will you put your skills, experience and passion to work toward your goals? 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. Come do your best work alongside other innovative, driven professionals in this meaningful role.

SENIOR DATA SCIENTIST

Amgen is on a mission to unlock the potential of biology for patients suffering from serious illnesses by discovering, developing, manufacturing, and delivering innovative human therapeutics. The Amgen R&D organization supports this mission by discovering, developing, and delivering transformative medicines that address the leading causes of death and disability. The R&D AI Strategy & Execution (RAISE) team was recently established to harness AI and emerging digital technologies to accelerate R&D priorities. The Data Science & Engineering (DS&E) team in RAISE is multidisciplinary group tasked with developing and deploying AI capabilities to accelerate execution of R&D strategic imperatives, which span discovery, development, patient safety, regulatory affairs, and our medical affairs. Data scientists in the DS&E team typically have advanced degrees in a STEM discipline of interest in biopharmaceutical R&D in addition to machine learning and advanced analytics experience.
We are seeking a Senior Data Scientist to join the RAISE DS&E Team. Candidates should possess robust knowledge and hands-on experience in developing statistical and machine learning models and using them to drive business value. In addition to general supervised and unsupervised ML, subareas of interest include natural language processing, image & audio processing, causal modeling, time series analysis, and generative AI. Additional capabilities in mechanism-based mathematical modeling, advanced statistical modeling, or causal modeling may receive special consideration. Domain knowledge in at least one biopharmaceutical R&D discipline is strongly preferred.

BASIC QUALIFICATIONS:

• Doctorate degree

OR

• Master’s degree and 3 years of experience in a quantitative field

OR

• Bachelor’s degree and 5 years of experience in a quantitative field

OR

• Associate degree and 10 years of experience in a quantitative field

OR

• High school diploma/GED and 12 years of experience in a quantitative field.

PREFERRED QUALIFICATIONS:

• Advanced degree in a quantitative field (MS, PhD, or equivalent experience).
• Experience in designing, evaluating, and applying a variety of supervised and unsupervised machine learning models to drive business value.
• Proficiency in the use of one (or more) of these methodologies with demonstrated and interpretable insight and impact: Deep learning, Transformer models, NLP models, Time Series Models, generative artificial intelligence (Gen AI), Bayesian Models.
• Experience with Healthcare data, e.g., clinical trial data, electronic medical records, and insurance claims; or Biosciences data, e.g., protein or small molecule data, or bioinformatics; or Biopharmaceutical manufacturing.
• Experience using causal modeling to inform decision making in a scientific, medical, or business setting.
• Proficiency in Python and SQL.
• Experience with cloud computing technologies, e.g., AWS, Spark.
• Experience with source code control technologies, e.g., Git.
• Full stack experience with building and deploying data engineering or modeling pipelines.
• Experience leading project teams of data scientists and machine learning engineers.

Let’s do this. Let’s change the world. In this vital role you will work collaboratively across disciplines with an overt sense of ownership.

• Fuel innovation and create initiatives by bringing to bear an understanding of biopharmaceutical, healthcare, and technology ecosystems.
• Be a technical guide and career development mentor to junior data scientists and machine learning engineers in a formal or matrixed fashion.
• Transform business, medical, or scientific questions into analytical ones and map out solutions, delivery, and impact.
• Communicate effectively and influence a diverse set of technical, scientific, medical, and business constituents at the functional and executive levels.
• Employ unsupervised and supervised techniques to develop predictive or prescriptive models with reliable performance, interpretability, and actionability.
• Develop or lead the deployment of generative models for a variety of applications.
• Help further build the team, including by contributing ideas and standard methodologies, and keeping abreast of developments in industry and academia.