Machine Learning Scientist II

2 weeks ago

Portland, United States Cambia Health Solutions Full time


Machine Learning Scientist II

Remote or Hybrid or On-site within OR, WA, ID or UT

Cambia Health Solutions is working to create a seamless and frictionless health care experiencefor consumers nationwide. This presents a unique challenge and opportunity for innovative solutions that serve patients and providers and influence the healthcare system. Cambia's AI team builds, prototypes, and deploys data-driven models and algorithms to production systems, delivering more equitable, effective, and affordable health care to our members.

We are seeking a talented and skilled Machine Learning Scientist II to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better health care. We contribute broadly across Cambia, working on a wide range of challenging problems, for instance:

  • Reducing our members' claim costs using both supervised and unsupervised approaches.
  • Speeding up prior authorizations and appeals using NLP to understand clinical notes.
  • Personalizing member engagement to promote the health and well-being of our members.
  • Driving health equity across Cambia initiatives.
  • And much more

As a Machine Learning Scientist II, you will play a vital role in understanding requirements, prototyping and building models, conducting experiments, and driving innovative solutions. Your passion for machine learning, deep learning, and NLP, coupled with your eagerness to learn and grow, will be instrumental in advancing Cambia's data-driven initiatives.

Qualifications & Requirements:

  • Academic degree (master's or PhD preferred) in Data Science, Computer Science, Statistics, or a related field.
  • Machine learning: Strong mathematical foundation and understanding of the concepts underlying machine learning, deep learning, NLP, statistical modeling, and data analysis. Familiarity with common machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. Understanding of standard algorithms (e.g., search & sort) and data structures, and their analysis.
  • NLP: Expertise in NLP and experience using LLM and NLP libraries like NLTK, spaCy, or Hugging Face transformers is a plus.
  • Model development and evaluation: Experience applying a variety of ML techniques and approaches to solve problems. Strong foundation in model evaluation, including metric development and selection.
  • Familiarity with production systems: Basic understanding of software engineering principles and considerations for deploying ML models in production systems. Exposure to containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP) is helpful. Understanding of model monitoring and MLOps.
  • Data preprocessing and analysis: Understanding of how to structure machine learning pipelines. Familiarity with data preprocessing techniques and tools. Experience with SQL and/or python data processing libraries (e.g., Pandas, NumPy).
  • Analytical mindset: Strong analytical thinking and problem-solving abilities to contribute to data analysis and experimental evaluations. Attention to detail and an eagerness to learn from experimental results.
  • Communication and teamwork: Good communication skills to collaborate effectively with cross-functional teams. Willingness to collaborate and learn with team members.
  • Healthcare knowledge: Previous experience is beneficial but not required.



Responsibilities:

  • Model prototyping and development: Use machine learning, deep learning, and NLP to prototype, develop, and refine models on top of our ML platform, leveraging best practices and established frameworks. Implement algorithms and techniques to meet requirements and objectives of specific business problems.
  • Experimentation and evaluation: Conduct experiments and evaluations to assess the performance and effectiveness of different models and techniques. Develop metrics that reflect the needs of the business for their use cases. Analyze experimental results, interpret findings, and provide actionable recommendations.
  • Model deployment and productionization: Work with ML Engineers to optimize and adapt models for real-time, scalable, and efficient performance. Collaborate with engineering and infrastructure teams to ensure seamless integration and deployment of models into production systems.
  • Requirement analysis and solution design: Collaborate with cross-functional teams to understand business requirements, define clear objectives, and develop technical plans. Work with stakeholders to identify opportunities where machine learning techniques can provide valuable insights and solutions.
  • Data preprocessing and feature engineering: Implement robust and reusable data preprocessing and feature engineering pipelines to extract meaningful insights from raw data. Clean, transform, and prepare datasets to facilitate effective model training and evaluation.
  • Continuous learning and innovation: Stay updated with the latest advancements in machine learning, deep learning, and NLP, particularly as applied in healthcare. Explore and evaluate new algorithms, frameworks, and tools to enhance model performance and efficiency.



Work Environment:

  • Work primarily performed in a hybrid environment consisting of in-office and working from home.
  • Travel may be required, locally or out of state.

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