AI Scientist ; Healthcare

AI SCIENTIST I (HEALTHCARE)

Hybrid (Office 3 days/wk – Onsite-Flex) within Oregon, Washington, Idaho or Utah.

Build a career with purpose. Join our Cause to create a person-focused and economically sustainable health care system.

Every day, Cambia’s Applied AI Team is living our mission to make health care easier and lives better. AI Scientists work with various stakeholders to design, develop, and implement data-driven solutions. This position applies expertise in advanced analytical tools such as generative AI, machine learning, deep learning, optimization, and statistical modeling to solve business problems in the healthcare payer domain. AI Scientists may focus on a particular area of the business such as clinical care delivery, customer experience, or payment integrity, or they may work across several areas spanning the organization.

In addition to expertise in generative AI, machine learning, deep learning and analytics this role requires knowledge of data systems, basic software development best practices, and algorithm design.

AI Scientists work closely with AI team members in the Product and Engineering tracks to collaboratively develop and deliver models and data-driven products. AI Scientists also collaborate and communicate with business partners to design and develop data-driven solutions to business problems and interpret and communicate results to technical and non-technical audiences – all in service of making our members’ health journeys easier.

If you’re a motivated and experienced AI Scientist looking to make a difference in the healthcare industry, apply for this exciting opportunity today!

• Bachelor’s degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics
• 0-3 years of related work experience
• Equivalent combination of education and experience

• Demonstrated knowledge of generative AI, machine learning and data science.
• Ability to use well-understood techniques and existing patterns to build, analyze, deploy, and maintain models.
• Effective in time and task management.
• Able to develop productive working relationships with colleagues and business partners.
• Strong interest in the healthcare industry.

• Generative AI:
  Understanding of foundation models, transformer architectures, and techniques for working with large language models (LLMs). Experience with prompt engineering, fine-tuning approaches, and evaluation methods for generative models.
• Machine Learning:
  Strong mathematical foundation and theoretical grasp of the concepts underlying machine learning, optimization, etc. Demonstrated understanding of how to structure simple machine learning pipelines (e.g., has prepared datasets, trained and tested models end-to-end).
• Data:
  Strong foundation in data analysis.
• Programming:
  Strong python programming skills. Familiarity with standard data science packages. Familiarity with standard software development best practices. Strong SQL skills a plus.
• Algorithms:
  Understanding of standard algorithms and data structures (ex. search and sort) and their analysis.

• Large Language Models (LLMs) and their capabilities (e.g., in-context learning, few-shot learning, zero-shot learning)
• Prompt engineering techniques and best practices
• Fine-tuning approaches (e.g., full fine-tuning, parameter-efficient methods like LoRA, QLoRA)
• Retrieval-Augmented Generation (RAG) and knowledge integration
• Evaluation methods for generative models (e.g., perplexity, BLEU, ROUGE, human evaluation)
• Alignment techniques (e.g., RLHF, constitutional AI, red-teaming)
• Multimodal generative models (text-to-image, text-to-video, multimodal understanding)
• Responsible AI considerations specific to generative models (e.g., bias, hallucinations, safety)
• Familiarity with Gen AI frameworks and tools (e.g., Hugging Face and Lang Chain)

• Classic ML algorithms (e.g., linear and logistic regression, decision and boosted trees, SVM, collaborative filtering, ranking)
• Approac…