Career Category
Information Systems
Job Description
As a Principal 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
6 –12 years of experience in Data Science (eg: managing structured and unstructured data, applying statistical techniques and reporting results)
Doctorate 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.
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