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 tests and experiments.
- Experience possessing deep functional and technical understanding of the Machine Learning technologies (Google’s Cloud Platform, custom and COTS-embedded) and provide prescriptive guidance on how these are leveraged within the Commercial Technologies and/or Business landscape.
- Experience mining and analyzing large structured and unstructured datasets.
- Experience identifying the data attributes that influence the outcome, define, and monitor metrics, create data narratives, and builds tools to drive decisions.
- Experience in building end-to-end ML pipelines from data ingestion, feature engineering, model training, deploying and scaling the model in production
- Experience in model training, model evaluation, model optimization, ML system architecture design, and scalable ML model deployment
- Experience building large-scale batch and real-time data pipelines with data processing frameworks like Scio, Google Cloud Platform and the Apache Beam
- Proficiency in Python and relevant libraries for machine learning such as scikit-learn and Pandas, as well as Jupyter Notebooks.
- Experience in building solutions for AI/ML services and platforms with models in production, ML Ops, CI/CD automation of ML pipelines in a cloud-based environment e.g., (GCP)
- Experience interacting with REST APIs and microservices
- Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment.
What is expected of you and others at this level
- Applies advanced knowledge and understanding of concepts, principles, and technical capabilities to manage a wide variety of projects
- Recommends new practices, processes, metrics, or models
- Works on complex projects of large scope projects may have significant and long-term impact
- Provides solutions which may set precedent
- Collaborate with stakeholders for completion of new projects
Anticipated salary range: $93,500 - $133,600
Bonus eligible: No
Benefits: Cardinal Health offers a wide variety of benefits and programs to support health and well-being.
- Medical, dental and vision coverage
- Paid time off plan
- Health savings account (HSA)
- 401k savings plan
- Access to wages before pay day with myFlexPay
- Flexible spending accounts (FSAs)
- Short- and long-term disability coverage
- Work-Life resources
- Paid parental leave
- Healthy lifestyle programs
Application window anticipated to close: 11/26/2024 *if interested in opportunity, please submit application as soon as possible.
The salary range listed is an estimate. Pay at Cardinal Health is determined by multiple factors including, but not limited to, a candidate’s geographical location, relevant education, experience and skills and an evaluation of internal pay equity.
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Candidates who are back-to-work, people with disabilities, without a college degree, and Veterans are encouraged to apply.
Cardinal Health supports an inclusive workplace that values diversity of thought, experience and background. We celebrate the power of our differences to create better solutions for our customers by ensuring employees can be their authentic selves each day. Cardinal Health is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, ancestry, age, physical or mental disability, sex, sexual orientation, gender identity/expression, pregnancy, veteran status, marital status, creed, status with regard to public assistance, genetic status or any other status protected by federal, state or local law.
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