About this role
About the Role As the Senior Associate Data Scientist at Amgen, you will be responsible for developing and deploying advanced machine learning, operational research, semantic analysis, and statistical methods to uncover structure in large data sets. This role involves creating analytics solutions to address customer needs and opportunities.
What You'll Do
- Work on upgrades and manage the execution of Proprietary AI engine built to optimize Copay and other GTN initiatives.
- Ensure models are trained with the latest data and meet the SLA expectations.
- Act as a subject matter expert in solving development and commercial questions.
- Work with a global cross-functional team on the AI tool’s road map.
- Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
- Utilize statistical techniques such as hypothesis testing, machine learning, and retrieval processes to identify trends and analyze data.
- Perform exploratory and targeted data analyses using descriptive statistics and other methods.
- Model/analytics experiment and development pipeline leveraging MLOps.
- Collaborate with technical teams to translate business needs into technical specifications, focusing on AI-driven automation and insights.
- Develop and integrate custom applications, intelligent dashboards, and automated workflows incorporating AI capabilities to enhance decision-making and efficiency.
What We're Looking For
- Experience with one or more analytic software tools or languages like R and Python.
- Foundational understanding of the US pharmaceutical ecosystem and patient support services offerings (Copay) and other standard datasets including claims and prescriptions.
- Strong foundation in machine learning algorithms and techniques.
- Experience with statistical techniques including hypothesis testing, regression analysis, clustering and classification.
Nice to Have
- Experience with MLOps tools (MLflow, Kubeflow, Airflow) and DevOps tools (Docker, Kubernetes, CI/CD).
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).
- Excellent analytical and problem-solving skills; ability to learn quickly; strong communication.
- Experience with data engineering and pipeline development.
- Knowledge of NLP techniques for text and sentiment analysis.
- Experience in time-series forecasting and trend analysis.
- Cloud experience (AWS, Azure, or Google Cloud) and Databricks for analytics and MLOps.
- Professional certifications (AWS Developer, Python and ML) preferred.
Compensation & Benefits
- Salary range and benefits not disclosed in the posting.