AI Scientist II (Healthcare)

Location

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

Compensation

Expected hiring range: $114k–$145k/year.

Bonus target: 15%.

Current full salary range: $114K – $186K/year.

Responsibilities

• Research, design, develop and implement data‑driven models and algorithms using generative AI, machine learning, deep learning, and standard statistical techniques.
• Train and test models, and develop algorithms to solve business problems.
• Adhere to standard best‑practices and establish principled experimental frameworks for developing data‑driven models.
• Develop models and perform experiments and analyses that are replicable by others.
• Use open‑source packages and managed services when appropriate to facilitate model development.
• Identify, measure, analyze, and visualize drivers to explain model performance (e.g., feature importance, interpretability, bias and error analysis), both offline (in the development phase) and online (in production).
• Use appropriate metrics and quantified outcomes to drive AI model and algorithm improvements.
• Generate new features by following examples, using SQL or SQL‑like code.
• Work effectively with data that may be noisy, high dimensional, sparse and/or imbalanced.
• Contribute to the full life‑cycle of modeling, from training or fine‑tuning, to model evaluation, to model deployment.
• Build robust production‑grade AI pipelines using tools and patterns in the AI platform.
• Access and process structured and unstructured data in various databases and formats.
• Assess new AI and ML capabilities and adapt them to our problems and environment.
• Identify or develop appropriate model metrics and objective functions to ensure models satisfy stated business requirements and KPIs.
• Proactively identify potential pitfalls and risks and develop ways to avoid them.
• Play a role in ensuring that the work being carried out by the AI team has sufficient business value.
• Write clean, well‑commented, efficient Python code.

Qualifications and Certifications

• Degree (master’s or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics.
• 4 years of related work experience.
• Equivalent combination of education and experience.

Skills and Attributes (Not Limited To)

• 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.
• Ability to code effectively in and create novel features and datasets using SQL or SQL‑like languages.
• Ability to write clean, well‑commented, efficient Python code.
• Strong understanding of techniques for working with noisy, high‑dimensional, sparse, and/or imbalanced data.
• Demonstrates in‑depth familiarity with at least one domain of data (e.g., claims data).
• Demonstrates depth of understanding in at least one major AI modeling technique or approach.
• Ability to develop new AI pipelines for both offline testing and online serving of models.
• Demonstrated track record of delivery of AI and machine learning models to solve well‑defined business problems.
• Has working knowledge of department processes, procedures, and infrastructure.
• Able to identify common pitfalls in developing AI and ML models (e.g., data leakage across features or partitions).
• Ability to translate business requirements into data science and AI discovery plans and modeling objectives.
• Ability to articulate the high‑level business objectives of their work.
• Performs a range of data science tasks with a moderate level of guidance and direction.
• Ability to partner within and across departments to remove blocks and achieve results.

Core Knowledge – Generative AI

• 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, LangChain).

Core Knowledge – Machine Learning

• 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.
• Deep learning (basic understanding expected at all levels).
• Activation functions.
• Optimization/Gradient Descent.
• Common architectures (CNN, RNN, LSTM, GAN, etc.).
• Embeddings.
• Familiarity with specializations (sequence modeling/NLP/computer vision).

Core Knowledge – Math

• Linear Algebra.
• Discrete math.
• Probability and Statistics.
• Calculus.

Core Knowledge – Data

• Research and experiment design.
• Visualization with data.
• Answering questions with data.

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.

Equal Opportunity Employer

We are an Equal Opportunity employer dedicated to a drug and tobacco‑free workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, religion, age, sex, sexual orientation, gender identity, disability, protected veteran status or any other status protected by law.

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