Machine Learning Scientist II
Company: Cambia Health Solutions, Inc
Location: Portland
Posted on: April 17, 2024
Job Description:
Machine Learning Scientist IIRemote or Hybrid or On-site within
OR, WA, ID or UTCambia Health Solutions is working to create a
seamless and frictionless health care experience for 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.
Keywords: Cambia Health Solutions, Inc, Beaverton , Machine Learning Scientist II, Other , Portland, Oregon
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