AI Scientist Intern at Cambia Health Solutions in Burlington, WA

Summary: Cambia Health Solutions is a purpose-driven company in the healthcare sector, seeking an AI Scientist Intern for a 12-week full-time position starting in May or June 2026. The intern will work under the mentorship of an AI Scientist, engaging in the design, development, and implementation of data-driven solutions using machine learning and statistical modeling techniques to address business problems in healthcare. Responsibilities: - Researches, designs, develops, and implements data-driven models and algorithms using machine learning, deep learning, statistical, and other mathematical 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 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 model and algorithm improvements - Analyzes, diagnoses, and resolves bugs in production machine learning models and systems - Evaluates model/use case feasibility by quickly generating prototypes - Takes models from prototype stage and improves performance as needed - Writes clean, well-commented, tested, version-controlled, and maintainable python code - Collaborates with team members and Cambia business partners - Actively participates in group meetings and discussions - Communicates effectively both orally and in writing with both technical and non-technical audiences - Keeps current with the state of the art in machine learning and AI and its application to healthcare - Keeps current with evolving commercial and open-source tools, techniques, and brings these practices to projects - Over time develops familiarity and insight with various subdomains of healthcare data Required Qualifications: - Demonstrated knowledge of data science, machine learning, and modeling - 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 read, understand, and apply the latest research to enhance our products where possible - Strong mathematical foundation and theoretical grasp of the concepts underlying machine learning, optimization, etc - Demonstrated understanding of how to structure simple machine learning pipelines - 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 overfitting - 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 - Experience with natural language processing (NLP) techniques such as tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and machine translation - Familiarity with pre-trained models and frameworks like BERT, GPT, and spaCy for NLP tasks - Understanding of text preprocessing, feature extraction, and embedding methods - Experience with NLP libraries and tools (NLTK, Hugging Face Transformers, etc.) - Capable of building and refining NLP models for tasks such as text classification, language generation, and information extraction - Awareness of the ethical considerations and challenges in deploying NLP models - 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, LLM as a Judge) - 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) - Strong foundation in data analysis - Research and experiment design - Visualization with data - Answering questions with data - Strong python programming skills. Familiarity with standard data science packages - Familiarity with standard software development best practices. Strong SQL skills a plus - Understanding of standard algorithms and data structures (ex. search & sort) and their analysis Required Skills: Data Science, Machine Learning, Deep Learning, Statistical Modeling, Healthcare Payer Domain, Algorithmic Design, Python Programming, Machine Learning Pipelines, Classic Machine Learning Algorithms, Supervised Learning, Semi-supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression, Classification, Time Series Modeling, Transfer Learning, Objective Functions, Regularization, Overfitting, Hyperparameter Tuning, Model Evaluation Metrics, Feature Importance, Scikit-learn, XGBoost, TensorFlow, PyTorch, Natural Language Processing, Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, Machine Translation, Pre-trained NLP Models, BERT, GPT, spaCy, Text Preprocessing, Feature Extraction, Embedding Methods, NLTK, Hugging Face Transformers, Text Classification, Language Generation, Information Extraction, Large Language Models, Prompt Engineering, Fine-tuning Techniques, Retrieval-Augmented Generation, Generative Model Evaluation, Alignment Techniques, Multimodal Generative Models, Responsible AI, Data Analysis, Research Design, Data Visualization, SQL, Algorithms, Data Structures, Interest in Healthcare Industry Benefits: Generous benefits, Work from home options, Participating in Cambia-supported outreach programs