AI Scientist Senior II

Hybrid role (3 days/week in office) at our Burlington, Renton, Spokane, Vancouver, Portland, Medford, Salt Lake City, Boise, Lewiston, or Fargo offices. Candidates must reside within commutable distance of that location or be willing to relocate.

About the role

Join our Cause to create a person-focused and economically sustainable health care system. Every day, Cambia's Applied AI Team is living our mission to make health care easier and lives better. AI Scientists work with various stakeholders to design, develop, and implement data-driven solutions. This position applies deep expertise in advanced analytical tools such as generative AI, machine learning, deep learning, optimization, and statistical modeling to solve complex, high-impact business problems in the healthcare payer domain.

As a Senior II AI Scientist, you will serve as a technical leader and strategic advisor, driving innovation across multiple business areas such as clinical care delivery, customer experience, and payment integrity. This is a hands-on technical leadership role – you will personally architect and build sophisticated AI solutions while simultaneously mentoring junior team members and influencing the technical direction of our AI initiatives. You will lead by example, writing production-quality code, conducting rigorous experiments, and demonstrating best practices in every aspect of AI development.

Responsibilities

• Lead the design and architecture of complex, multi-component AI systems that solve strategic business problems, while defining technical standards, best practices, and design patterns for AI development across the team.
• Evaluate and recommend new AI technologies, frameworks, and methodologies for adoption, serving as the technical authority on AI/ML topics.
• Drive innovation by researching and prototyping cutting-edge AI techniques applicable to healthcare challenges, and lead technical design reviews to ensure high-quality solutions.
• Research, design, and implement novel AI solutions using state-of-the-art generative AI, machine learning, and deep learning techniques to handle complex, real-world healthcare data challenges.
• Design custom algorithms and modeling approaches when existing solutions are insufficient, and develop advanced evaluation frameworks that capture business value and model behavior.
• Create reusable components, libraries, and frameworks that accelerate AI development, and lead the development of production-grade AI systems with robust monitoring, governance, and maintenance strategies.
• Partner with business leaders to identify high-impact AI opportunities and translate ambiguous business challenges into well-defined AI problems with clear success criteria.
• Design comprehensive experimentation strategies including A/B testing, causal inference, and statistical validation.
• Proactively identify risks, biases, and ethical considerations in AI solutions and develop mitigation strategies, while quantifying and communicating business impact and ROI to executive stakeholders.
• Design and optimize complex data pipelines for model training, evaluation, and serving, while developing advanced feature engineering strategies that unlock model performance.
• Build scalable, maintainable AI systems using modern MLOps practices and cloud infrastructure, with comprehensive monitoring and observability for production systems.
• Ensure data quality, governance, and compliance with healthcare regulations (HIPAA, etc.).
• Mentor junior and mid-level AI Scientists, providing technical guidance and career development support through code reviews and constructive feedback.
• Lead knowledge-sharing sessions, workshops, and technical presentations, while contributing to hiring and onboarding processes.
• Foster a culture of continuous learning, experimentation, and technical excellence.
• Lead cross-functional initiatives involving Product, Engineering, and other departments.

Requirements

• Degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and typically at least 12 years of related work experience. Equivalent combination of education and experience will be considered.

Skills

Technical Leadership & Strategy

• Recognized expert in generative AI, machine learning, and data science with ability to architect complex, novel solutions and define technical vision aligned with business strategy.
• Deep understanding of the healthcare industry (preferred) with ability to identify and prioritize high-value AI opportunities, evaluate emerging technologies, and lead multiple complex projects from conception to production.

Advanced Technical Expertise

• Mastery of advanced AI/ML techniques with ability to innovate beyond existing patterns, combined with expert-level Python programming and strong software engineering principles (design patterns, testing, CI/CD).
• Deep expertise in working with complex, real-world data challenges (noisy, high-dimensional, sparse, imbalanced, biased) across multiple data domains (e.g., claims, clinical, member engagement).
• Deep expertise in multiple AI modeling techniques with ability to select and combine methods innovatively, design scalable architectures for offline and online systems, and implement MLOps, model governance, and responsible AI practices.
• Advanced SQL and data engineering skills, including optimization of complex queries and data pipeline design.

Problem Solving & Innovation

• Ability to tackle ambiguous, ill-defined problems and structure them into actionable AI initiatives that create measurable business value.
• Proactive identification of AI opportunities for strategic advantage, with ability to anticipate technical risks, design mitigation strategies, and conduct research and experimentation including A/B testing and causal inference.

Leadership & Collaboration

• Proven ability to mentor and develop junior AI Scientists while establishing and evangelizing best practices, coding standards, and technical processes.
• Strong leadership presence with ability to influence technical decisions across the organization, lead cross-functional teams, manage stakeholder relationships, and build productive partnerships across departments.
• Excellent communication skills with ability to present complex technical concepts to audiences ranging from technical teams to C-level executives.

Business Acumen

• Strong ability to translate business strategy into AI opportunities and technical requirements, quantify business impact and ROI of AI initiatives, and balance technical excellence with pragmatic business delivery.
• Understanding of healthcare payer operations, regulations, and industry trends.

Core Knowledge

• Generative AI Foundation Models & Architectures: Deep understanding of transformer architectures, attention mechanisms, scaling laws, and experience with multiple model families (GPT, BERT, etc.).
• Advanced Fine-tuning & Prompt Engineering: Expertise in parameter-efficient fine-tuning (LoRA, QLoRA, Adapters), instruction tuning, domain adaptation, and advanced prompting techniques (chain-of-thought, tree-of-thought, meta-prompting).
• RAG & Agent Systems: Advanced RAG architectures, hybrid search strategies, knowledge base optimization, and experience designing AI agent systems with tool use, planning, and multi-agent collaboration.
• Evaluation, Alignment & Production: Deep expertise in evaluation methodologies (automated metrics, LLM-as-judge, human evaluation), alignment techniques (RLHF, DPO, constitutional AI), inference optimization, caching strategies, and cost management.
• Multimodal & Responsible AI: Experience with vision-language models and multimodal understanding, plus deep understanding of bias detection and mitigation, hallucination reduction, safety considerations, and privacy-preserving techniques.
• Frameworks & Tools: Expert-level proficiency with Hugging Face ecosystem, LangChain, LlamaIndex, vector databases, and emerging GenAI tools.

Machine Learning

• Advanced Algorithms & Methods: Deep expertise across supervised, unsupervised, semi-supervised, and reinforcement learning paradigms, including ensemble methods (boosting, bagging, stacking), time series forecasting, and causal inference.
• Optimization & Evaluation: Deep understanding of optimization algorithms, convergence properties, custom loss function design, experimental design, statistical testing, and bias-variance tradeoff analysis.
• AutoML & Transfer Learning: Experience with automated model selection, hyperparameter optimization at scale, and advanced techniques for knowledge transfer and few-shot learning.

Deep Learning

• Advanced Architectures & Optimization: Deep understanding of CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, and diffusion models, plus advanced optimization methods, learning rate scheduling, and convergence analysis.
• Regularization & Specialized Domains: Advanced techniques including dropout variants, batch normalization, layer normalization, and architectural regularization, with expertise in NLP, computer vision, or speech processing.
• Model Compression: Knowledge of quantization, pruning, distillation, and efficient inference techniques.

Mathematics

• Core Mathematical Foundations: Advanced linear algebra (matrix decompositions, eigen analysis, numerical methods), probability and statistics (Bayesian methods, hypothesis testing, experimental design), optimization theory (convex optimization, constrained optimization, stochastic optimization), and information theory.

Data & Software Engineering

• Data Architecture & SQL: Understanding of data warehousing, data lakes, modern data stack components, plus advanced SQL including query optimization, window functions, CTEs, and performance tuning.
• Software Engineering & MLOps: Design patterns, testing strategies (unit, integration, end-to-end), version control, CI/CD, model versioning, experiment tracking, model monitoring, and deployment strategies.
• Distributed Computing & Cloud: Experience with distributed training, data parallelism, scalable data processing (Spark, Dask, Ray), and proficiency with cloud AI/ML services (AWS SageMaker, Azure ML, GCP Vertex AI).