Senior Machine Learning Scientist

Cambia Health Solutions is working to create a seamless and frictionless healthcare 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 Applied 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 highly-skilled and experienced Senior Machine Learning Scientist 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. In this role, we are seeking a technical leader for our Healthcare Services Operations AI team working on use cases such as

• Streamline prior authorization and appeal reviews using GenAI and NLP.

• Summarizing medical policy decisions in plain language for our members.

• Identifying members at risk for certain conditions to offer them opportunities for high-value care.

• And much more

As a Senior ML Scientist on our team, you will play a crucial role in leading the team 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 curiosity and desire to keep learning, will be instrumental in advancing Cambia's data-driven initiatives. In collaboration with your fellow ML Scientists, our AI product team, and our partner data engineering and software development teams, you will bring ML models to production and maintain their health and integrity while in production. Your expertise in theoretical machine learning, coupled with your practical experience engineering ML systems, will be instrumental in driving the success of Cambia's AI/ML initiatives.

Qualifications and Requirements

• Academic degree (masters or PhD preferred) in Computer Science, Engineering, or a related field.

• Minimum of 7 years of experience in ML development and deploying ML solutions in cloud-based production environments for a Senior MLS I

• Minimum of 9-12 years of experience in ML development and deploying ML solutions in cloud-based production environments for a Senior MLS II

• Machine learning: Strong mathematical foundation and understanding of the concepts underlying classic machine learning, deep learning, NLP, statistical modeling, and data analysis. Expert-level familiarity with common machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc.

• Natural Language Processing (NLP): Expertise in NLP and experience using NLP libraries like NLTK, SpaCy, Gensim, etc. is a strong plus.

• Generative AI and Large Language models (strongly preferred): Understanding and experience working with language (LLM) models and generative AI, including encoder-only (BERT Family) and autoregressive models (GPT family). Experience using different frameworks and libraries including but not limited to huggingface, Langchain, Llamaindex, vector databases.

• Model development and evaluation: Experience in and deep intuition for applying ML techniques and approaches to solve problems. Strong foundation in model evaluation, including metric development and selection.

• Software development skills: Solid understanding of software engineering principles, data structures, and algorithms. Expert-level proficiency in Python.

• ML production systems: Experience deploying ML models in production systems. Exposure to containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP) is helpful. Strong grounding in 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.

• Continuous learning: Passion for staying up-to-date with the latest advancements in machine learning and data engineering. Proactive in learning new tools, techniques, and frameworks to drive innovation.

• Leadership: Strong leadership, coaching, and mentorship abilities. Ability to inspire and motivate team members towards achieving goals and delivering high-quality results.

• Responsible AI: Desire to adhere to ethical considerations and responsible AI practices in machine learning. Familiarity with fairness, bias mitigation, privacy, and security aspects of machine learning models.

• Healthcare knowledge: Previous experience is extremely helpful but not required.

Responsibilities:

• Technical leadership: Lead projects and mentor machine learning scientists and engineers. Provide technical guidance and model technical excellence. Foster a collaborative and inclusive team culture that encourages innovation, growth, and continuous learning.

• 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.

• 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 AI 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.

• Model monitoring and maintenance: Track the performance and impact of machine learning models and solutions in production settings. Report on KPIs and provide actionable insights to stakeholders. Continuously iterate and improve models based on feedback and real-world data.

• 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.

• Machine learning strategy: Contribute to and execute the organization's machine learning strategy. Identify and weigh in on areas where machine learning can provide the most value and competitive advantage.

• Compliance and ethical considerations: Ensure compliance with Responsible AI guidelines in the development and deployment of machine learning models. Promote fairness, transparency, and accountability in all aspects of machine learning initiatives.

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.