Career Category
Information Systems
Job Description
As a Senior Data Scientist, you will build and model digital product and platforms that bring Amgen’s AI/ML and GenAI solutions to life.
This role has to collaborate with Amgen’s Technical Architect, Product Manager, UX designers, and Back-end engineers to design secure, scalable, and user-centric products that accelerate discovery, manufacturing, and commercial analytics, corporate functions products
Key Responsibilities
- Strong experience with statistics and machine learning, including deep learning, natural language processing (NLP) and, experience in building cloud-scale systems and working with open-source stacks for data
- Experiment with large language models (LLM), Artificial intelligence (AI) for code or related fields, Generative AI, Foundational Models, Supervised and Unsupervised Learning
- Collaborate with cross-functional teams to understand the requirement and design solutions that meet business needs. Also with Data Architects, Business SMEs, and Technical Architect to design and develop to meet fast-paced business needs.
- Explore new tools and technologies that will help rapid development of solutions
- Participate in sprint planning meetings and provide estimations on technical implementation
- Embed responsible-AI and security-by-design controls
Required Qualifications
- 5–12 years of experience in Data Science (eg: managing structured and unstructured data, applying statistical techniques and reporting results)
- Doctorate or Master's or Bachelor's in Computer Science, Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science or a related field
- Leverage cloud platforms (AWS preferred) to build scalable and efficient solutions
- Strong background in Deep Learning, Machine Learning, NLP, Data Mining.
- Excellent communication and stakeholder management skills.
Preferred Skills
- Experience in Generative AI, Foundational Models, LLM's, Feature Engineering, Selection & Extraction, BI & Automation, Predictive Modelling, Data Visualization, CNN, RNN, GNN, Transformers, Exploratory Data Analysis
- Familiarity with Python packages (Pytorch, TensorFlow, Hugging FaceScikit-learn, Pandas, NumPy, Matplotlib, Cloud Vision API, RAG, TensorBoard, OpenCV,NLTK)), programming languages ( C, C++, Java, CUDA,SQL, NoSQL, PHP, HTML, JS, CSS etc.)
- Prior exposure to pharma / life sciences AI environments is preferred.
- Strong understanding of Responsible AI and model validation principles.
.