Full Stack Data Scientist

At Cardinal Health's Artificial Intelligence Center of Excellence (AI CoE), we're focused on using technology to improve healthcare.  Our commitment to innovation, design, and a product-centric approach helps us create solutions that make a real difference.

We're a team of passionate individuals who thrive in a culture of collaboration and continuous learning. We leverage cutting-edge technology and data insights to solve complex problems, forge new business models, and create products that truly impact the lives of our customers.

As a Full Stack Data Scientist & Machine Learning Engineer and a key member of our AI CoE, you'll play a pivotal role in driving this transformation. You'll work closely with business stakeholders to understand their needs and translate them into actionable data-driven solutions. You will be responsible for building and maintaining robust machine learning models and GenAI solutions, designing intuitive user interfaces, and ensuring seamless integration with our existing systems

Responsibilities

• Develop and deploy Machine Learning (ML) models: Design, train, and optimize machine learning models for a variety of applications like forecasting, classification and categorization systems, and churn prediction.
• Develop, integrate and maintain Generative AI (GenAI) solutions: Explore and implement GenAI technologies, like large language models (LLMs), to enhance existing applications or create new GenAI-based solutions. This includes working with RAG technologies, embedding models, and crafting effective prompts for LLMs. Ensure the reliable and scalable deployment; and support and maintenance of GenAI models in production environments.
• Build user-facing applications: Develop intuitive and user-friendly web applications using modern front-end frameworks (e.g., React, Angular, Vue.js) to showcase and interact with your ML/GenAI solutions.
• Construct robust APIs: Design and implement RESTful APIs to integrate your ML models or GenAI solutions with other applications and systems within the organization.
• Build and maintain end-to-end ML pipelines: Design, develop, and maintain robust and scalable ML pipelines, encompassing data ingestion, feature engineering, model training, deployment, and monitoring.
• Ensure scalability and performance: Design and implement solutions to ensure that your ML and GenAI applications are scalable, efficient, and performant, even with large volumes of data and usage.
• Create compelling data visualizations: Develop interactive and insightful visualizations to communicate the results of your data analysis and ML/GenAI models to stakeholders
• Collaborate with cross-functional teams: Work closely with principal and senior data scientists, data engineers, business analysts, and product and project managers to ensure successful delivery of projects.
• Stay current on the latest trends in AI: Actively seek out and experiment on new ML and GenAI technologies and approaches to enhance your skillset, and the performance and efficiency of your ML/GenAI models. This includes active participation in internal events like AI CodeJam.

Qualifications 

• Bachelor’s degree in mathematics, Statistics, Engineering, Computer Science, other related field, or equivalent years of relevant work experience is preferred.
• At least 4 years of experience as a Full Stack Machine Learning Engineer or similar role preferred
• Experience including HTML, HTML5, CSS3 and JavaScript (React preferred), Angular, Vue.js, Python, Java, Node.js, Flask/Django, FastAPI, PostgreSQL.
• Experience in DevOps tools like Docker, Kubernetes, Airflow; version control using Git and CI/CD piplelines using Concourse
• Knowledge of clinical domain and datasets.
• Knowledge of REST, Apigee, Microservices preferred
• Experience in Generative AI, RAG implementation, re-ranking, Large Language Models (LLMs), LangChain, LlamaIndex, Hugging Face, Vector databases, Embedding models, Prompting techniques.
• Experience with Machine Learning and related technologies such as Jupiter Notebooks, RAG, NumPy, Pandas, Scikit-learn, Tensor-Flow, Pytorch, Supervised and Unsupervised learning, Deep learning, Model evaluation
• Understanding of cloud data engineering and integration concepts including GCP, Vertex AI, Cloud functions, Compute Engine, Cloud storage.
• Strong mathematical and statistical skills. 
• 2+ years in the Healthcare industry and knowledge of clinical data preferred. 
• Delivery experience with Google Cloud Platform preferred. 
• Agile development skills and experience preferred. 
• Experience designing and developing machine learning and deep learning solutions and systems. 
• Experience using statistical analysis to determine data modeling approach, training machine learning t