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 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.

This role is looking for a highly skilled and versatile engineer who can bridge the gap between machine learning and front-end development. The ideal candidate will have a strong understanding of both areas and be able to apply their knowledge to build innovative and user-friendly applications.

Responsibilities

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.

Strong understanding of React components, state management, and lifecycle methods.

Ability to build complex and performant user interfaces.

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.

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 with popular React libraries and tools (e.g., Redux, React Router, Styled Components).

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:

1/26/2025 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._

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