AI Scientist I (Healthcare)

Cambia Health Solutions • Medford, OR

4d • Hybrid

About The Position

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. Who We Are Looking For: 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 work 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!

Requirements

• 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. (see below). 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 LangChain)
• Classic ML algorithms (e.g, linear and logistic regression, decision and boosted trees, SVM, collaborative filtering, ranking)
• Approaches (e.g, supervised, semi-supervised, unsupervised, reinforcement learning, regression, classification, time series modeling, transfer learning)
• Foundational ML concepts such as objective functions, regularization and over fitting
• Data partitions (train/dev/test) and model development
• Hyperparameter tuning and grid search
• Evaluation concepts (metrics, feature importance, etc.)
• Familiarity with standard python packages (scikit-learn, XGBoost, TensorFlow, PyTorch, etc.)
• Familiarity with structure of machine learning pipelines
• Activation functions
• Optimization/Gradient Decent
• Common architectures (CNN, RNN, LSTM, GAN, etc.)
• Embeddings
• Familiarity with specializations (sequence modeling/NLP/computer vision)
• Linear Algebra
• Discrete math
• Probability and Statistics
• Calculus
• Research and experiment design
• Visualization with data
• Answering questions with data

Responsibilities

• Researches, designs, develops, and implements data-driven models and algorithms using generative AI, machine learning, deep learning, statistical, and other statistical modeling techniques.
• Trains and tests models, and develops algorithms to solve business problems.
• Adheres to standard best-practices and establishes principled experimental frameworks for developing data-driven models.
• Develops models and performs experiments and analyses that are replicable by others.
• Uses open-source packages and managed services when appropriate to facilitate model development
• Identifies, measures, analyzes, and visualizes drivers to explain model performance (e.g, feature importance, interpretability, bias and error analysis), both offline (in the development phase) and online (in production).
• Uses appropriate metrics and quantified outcomes to drive AI model and algorithm improvements.

Benefits

• Medical, dental and vision coverage for employees and their eligible family members, including mental health benefits.
• Annual employer contribution to a health savings account.
• Generous paid time off varying by role and tenure in addition to 10 company-paid holidays.
• Market-leading retirement plan including a company match on employee 401(k) contributions, with a potential discretionary contribution based on company performance (no vesting period).
• Up to 12 weeks of paid parental time off (eligibility requires 12 months of continuous service with Cambia immediately preceding leave).
• Award-winning wellness programs that reward you for participation.
• Employee Assistance Fund for those in need.
• Commute and parking benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Number of Employees: 5,001-10,000 employees