Job Summary
At Navista, our mission is to empower community oncology practices to deliver patient-centered cancer care. Navista, a Cardinal Health company, is an oncology practice alliance co-created with oncologists and practice leaders that offers advanced support services and technology to help practices remain independent and thrive. True to our name, our experienced team is passionate about helping oncology practices navigate the future.
We are seeking an innovative and highly skilled Senior Data Scientist with specialized expertise in Generative AI (GenAI), Large Language Models (LLMs), and Agentic Systems to join the Navista - Data & Advanced Analytics team supporting the growth of our Navista Application Suite and the Integrated Oncology Network (IoN). In this critical role, you will be at the forefront of designing, developing, and deploying advanced AI solutions that leverage the power of generative models and intelligent agents to transform our products and operations. You will be responsible for pushing the boundaries of what's possible, from foundational research to production-ready applications, working with diverse datasets and complex problem spaces, particularly within the oncology domain.
The ideal candidate will possess a deep theoretical understanding and practical experience in building, fine-tuning, and deploying LLMs, as well as architecting and implementing agentic frameworks. You will play a key role in shaping our AI strategy, mentoring junior team members, and collaborating with cross-functional engineering and product teams to bring groundbreaking AI capabilities to life, including developing predictive models from complex, often unstructured, oncology data.
Responsibilities
Research & Development: Lead the research, design, and development of novel Generative AI models and algorithms, including but not limited to LLMs, diffusion models, GANs, and VAEs, to address complex business challenges.
LLM Expertise: Architect, fine-tune, and deploy Large Language Models for various applications such as natural language understanding, generation, summarization, question-answering, and code generation, with a focus on extracting insights from unstructured clinical and research data.
Agentic Systems Design: Design and implement intelligent agentic systems capable of autonomous decision-making, planning, reasoning, and interaction within complex environments, leveraging LLMs as core components.
Predictive Modeling: Develop and deploy advanced predictive models and capabilities using both structured and unstructured data, particularly within the oncology space, to forecast outcomes, identify trends, and support clinical or commercial decision-making.
Prompt Engineering & Optimization: Develop advanced prompt engineering strategies and techniques to maximize the performance and reliability of LLM-based applications.
Data Strategy for GenAI: Work with data engineers to define and implement data collection, preprocessing, and augmentation strategies specifically tailored for training and fine-tuning generative models and LLMs, including techniques for handling and enriching unstructured oncology data (e.g., clinical notes, pathology reports).
Model Evaluation & Deployment: Develop robust evaluation metrics and methodologies for generative models, agentic systems, and predictive models. Oversee the deployment, monitoring, and continuous improvement of these models in production environments.
Collaboration & Leadership: Collaborate closely with machine learning engineers, software engineers, and product managers to integrate AI solutions into our products. Provide technical leadership and mentorship to junior data scientists.
Innovation & Thought Leadership: Stay abreast of the latest advancements in GenAI, LLMs, and agentic AI research. Proactively identify new opportunities and technologies that can enhance our capabilities and competitive advantage.
Ethical AI: Ensure the responsible and ethical development and deployment of AI systems, addressing potential biases, fairness, and transparency concerns.
Qualifications
8-12 years of experience as a Data Scientist or Machine Learning Engineer, with a significant focus on deep learning and natural language processing, preferred
Bachelor's degree in related field, or equivalent work experience, preferred
Proven hands-on experience with Generative AI models (e.g., Transformers, GANs, VAEs, Diffusion Models) and their applications.
Extensive experience working with Large Language Models (LLMs), including fine-tuning, prompt engineering, RAG (Retrieval Augmented Generation), and understanding various architectures (e.g., GPT, Llama, BERT, T5).
Demonstrated experience in designing, building, and deploying agentic systems or multi-agent systems, including concepts like planning, reasoning, and tool use.
Strong experience working with unstructured data, particularly in the oncology domain (e.g., clinical notes, pathology reports, genomic data, imaging reports), and extracting meaningful features for analysis.
Demonstrated ability to create and deploy predictive capabilities and models from complex datasets, including those with unstructured components.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
Experience with relevant libraries and tools (e.g., Hugging Face Transformers, LangChain, LlamaIndex).
Strong understanding of machine learning fundamentals, statistical modeling, and experimental design.
Experience with at least one cloud platforms ( GCP, Azure) for training and deploying large-scale AI models.
Excellent problem-solving skills, with the ability to tackle complex, ambiguous problems and drive solutions.
Strong communication and presentation skills, capable of explaining comp
Experience in the healthcare or life sciences industry, specifically with oncology data and research, highly preferred
Experience with MLOps practices for deploying and managing large-scale AI models, highly preferred
Familiarity with distributed computing frameworks (e.g., Spark, Dask), highly preferred
Experience contributing to open-source AI projects, highly preferred
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
Participates in the development of policies and procedures to achieve specific goals
Recommends new practices, processes, metrics, or models
Works on or may lead complex projects of large scope
Projects may have significant and long-term impact
Provides solutions which may set precedent
Independently determines method for completion of new projects
Receives guidance on overall project objectives
Acts as a mentor to less experienced colleagues
Anticipated salary range: $123,400 - $176,300
Bonus eligible: Yes
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: 02/15/2026 *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.
LI-Remote
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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