Senior Data Scientist

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.

• →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
• →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 and Causal AI
• →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.)

• 6–12 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.

• Prior exposure to pharma / life sciences AI environments is preferred.
• Strong understanding of Responsible AI and model validation principles.