This job was posted by https://idahoworks.gov : For more information, please see: https://idahoworks.gov/jobs/2337787

Machine Learning Scientist Intern

This internship is a 12 week, full-time position starting in May or June 2025

Remote within WA, ID, OR, and UT

Primary Job Purpose:

Machine Learning Scientists work with various stakeholders to design, develop, and implement data-driven solutions. This position applies expertise in advanced analytical tools such as machine learning, deep learning, optimization, and statistical modeling to solve business problems in the healthcare payer domain. Machine Learning 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 analysis, machine learning and deep learning, this role requires knowledge of data systems, basic software development best practices, and algorithmic design.

Machine Learning Scientists work closely with AI team members in the Product and Engineering tracks to collaboratively develop and deliver models and data-driven products. ML 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.

As an intern you would work under the mentorship of another Machine Learning Scientist on some/all parts of the AI/ML lifecycle. This can be a feature in an existing product or on Proof-Of-Concepts (POCs) for a new or existing initiative.

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

Minimum Requirements:

• 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 i dustry.
• Ability to read, understand, and apply the latest research to enhance our products where possible.

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).
• 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