Data Scientist (US Value & Access Insights)

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.

Roles & Responsibilities:

• Ability to 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 tools road map
• Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
• Utilize technical skills such as hypothesis testing, machine learning and retrieval processes to apply statistical and data mining techniques to identify trends, create figures, and analyze other relevant information.
• 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 the business needs into technical specifications, particularly focusing on AI-driven automation and insights.
• Develop and integrate custom applications, intelligent dashboards, and automated workflows that incorporate AI capabilities to enhance decision-making and efficiency.

Functional Skills:

Must-Have Skills (Not more than 3 to 4):

• Experience with one or more analytic software tools or languages like R and Python
• Foundational understanding of US pharmaceutical ecosystem and Patient support services offerings (Copay) and other standard data sets including claims, prescription
• Strong foundation in machine learning algorithms and techniques
• Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification

Good-to-Have Skills:

• Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)
• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
• Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills
• Experience with data engineering and pipeline development
• Knowledge of NLP techniques for text analysis and sentiment analysis
• Experience in analyzing time-series data for forecasting and trend analysis
• Experience with AWS, Azure, or Google Cloud
• Experience with Databricks platform for data analytics and MLOps

Professional Certifications (please mention if the certification is preferred or mandatory for the role):

• Any AWS Developer certification (preferred)
• Any Python and ML certification (preferred)

Soft Skills:

• Initiative to explore alternate technology and approaches to solving problems.
• Skilled in breaking down problems, documenting problem statements, and estimating efforts.
• Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills
• Ability to work effectively with global, virtual teams
• High degree of initiative and self-motivation.
• Ability to manage multiple priorities successfully.
• Team-oriented, with a focus on achieving team goals

Basic Qualifications:

• Masters degree in computer science, statistics or STEM majors with a minimum of 2 years of Information Systems experience OR
• Bachelors degree in computer science, statistics or STEM majors with a minimum of 4 years of Information Systems experience.