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Company

Cardinal Health · 6 hours ago

Full Stack Data Scientist - Nationwide

Texas, United States

Full-time

Remote

Mid Level

$94K/yr - $134K/yr

4+ years exp

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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.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningGenerative AIFull Stack DevelopmentPythonReactJavaScriptHTML5CSS3DockerKubernetesPostgreSQLREST APIsDevOpsCloud EngineeringAgile DevelopmentDeep LearningStatistical AnalysisAngularVue.jsFlaskDjangoNode.jsJupyter NotebooksNumPyPandasScikit-learnTensorFlowPyTorchGoogle Cloud PlatformCI/CD

Required

Bachelor’s degree in mathematics, Statistics, Engineering, Computer Science, other related field, or equivalent years of relevant work experience

At least 4 years of experience as a Full Stack Machine Learning Engineer or similar role

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 pipelines using Concourse

Knowledge of clinical domain and datasets

Strong mathematical and statistical skills

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 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

Preferred

Experience in Generative AI, RAG implementation, re-ranking, Large Language Models (LLMs), LangChain, LlamaIndex, Hugging Face, Vector databases, Embedding models, Prompting techniques

Knowledge of REST, Apigee, Microservices

2+ years in the Healthcare industry and knowledge of clinical data

Delivery experience with Google Cloud Platform

Agile development skills and experience

Benefits

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

Company

Cardinal Health

Glassdoor

3.7

Cardinal Health is a manufacturer and distributor of medical and laboratory products.

Founded in 1971

Dublin, Ohio, USA

10,001+ employees

https://www.cardinalhealth.com

H1B Sponsorship

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Distribution of Different Job Fields Receiving Sponsorship

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Trends of Total Sponsorships

2023 (112)

2022 (131)

2021 (144)

2020 (126)