Data Scientist - Data Modeling/analytics
Posted Updated
Job Description
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the worlds toughest diseases, and make peoples lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond whats known today.
ABOUT THE ROLE
Role Description:
As the Senior Associate Data Scientist at Amgen, you will be responsible for developing and deploying advanced machine learning models. This role will support the development of statistical, Bayesian, causal, and machine learning models that enhance forecasting capabilities and quantify uncertainty to guide decision-making across the company. The position is well suited to a curious and collaborative problem solver who is excited about applying forecasting methods, complex datasets, and modern analytical tools to build decision-support solutions that inform planning and business decision-making.
Roles Responsibilities:
- Analyze large, complex datasets using statistical modeling, forecasting, and analytics techniques to generate insights that support business decision-making.
- Support the development of simulation and scenario-analysis capabilities to better understand the data
- Contribute across the modeling lifecycle, including business problem framing, exploratory data analysis, feature engineering, model development, validation, deployment support, monitoring, and explainability.
- Collaborate with senior team members to evaluate and apply new tools and methodologies in forecasting, data science, and AI to business problems.
- Communicate analytical findings and model results clearly to technical and non-technical stakeholders.
- Ensure models are trained with the latest data and meet the SLA expectations
- 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.
Basic Qualifications:
- Masters OR Bachelors degree in computer science, statistics or STEM majors with a minimum of4years and maximum of 7 years of Information Systems experience.
Must-Have Skills
- Strong Python and SQL skills for data analysis, statistical modeling, and automation.
- Hands-on experience with machine learning, predictive analytics, and forecasting models.
- Solid knowledge of statistics, including hypothesis testing, descriptive statistics, and experimental analysis.
- Experience across the end-to-end model lifecycle: data exploration, feature engineering, model development, validation, deployment, monitoring, and explainability.
- Ability to work with large and complex datasets using data-mining and analytical techniques.
- Familiarity with MLOps practices, including model pipelines, versioning, deployment support, retraining, and performance monitoring.
- Experience building simulation or scenario-analysis models.
- Ability to translate business problems into technical requirements and analytical solutions.
- Experience developing AI-enabled dashboards, automated workflows, or data applications.
- Strong stakeholder communication skillsable to explain insights and model outcomes to both technical and business audiences.
- Experience working in cross-functional, global technical teams.
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 :
- 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 andself-motivation.
- Ability to manage multiple prioritiessuccessfully.
- Team-oriented, with a focus on achieving team goals
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.
Skills
- AI
- Airflow
- Analytics
- Automation
- AWS
- Azure
- CI/CD
- Cloud
- Data Analytics
- Data Engineering
- Data Mining
- Data Science
- Databricks
- DevOps
- Docker
- Feature Engineering
- GCP
- Kubeflow
- Kubernetes
- Machine Learning
- MLflow
- MLOps
- NLP
- Predictive Analytics
- Python
- PyTorch
- scikit-learn
- SQL
- Statistics
- TensorFlow
- Time Series