AI Scientist Senior II

About the role

Join our Cause to create a person-focused and economically sustainable health care system. This is a hands-on technical leadership role where you will architect and build sophisticated AI solutions while mentoring junior team members and influencing the technical direction of our AI initiatives.

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 and design novel AI solutions using state-of-the-art generative AI, machine learning, and deep learning techniques to handle complex, real-world healthcare data challenges.
• 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, and proactively identify risks, biases, and ethical considerations in AI solutions.
• Quantify and communicate business impact and ROI to executive stakeholders, and develop mitigation strategies for identified risks.
• 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.), and mentor junior and mid-level AI Scientists, providing technical guidance and career development support through code reviews and constructive feedback.
• Foster a culture of continuous learning, experimentation, and technical excellence, and contribute to hiring and onboarding processes.

Requirements

The ideal candidate should have a 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.

Qualifications

• 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.
• 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.
• 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.
• 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.

Skills and Attributes

• 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.
• 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: Advanced mathematical foundations (linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced algorithms & methods (supervised, unsupervised, semi-supervised, reinforcement learning paradigms), advanced optimization & evaluation (convergence properties, custom loss function design, experimental design, statistical testing, bias-variance tradeoff analysis), advanced autoML & transfer learning (automated model selection, hyperparameter optimization at scale, advanced techniques for knowledge transfer and few-shot learning), advanced deep learning architectures & optimization (CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, diffusion models), advanced regularization & specialized domains (dropout variants, batch normalization, layer normalization, architectural regularization), knowledge of model compression techniques (quantization, pruning, distillation, efficient inference techniques), core mathematical foundations (advanced linear algebra, probability and statistics, optimization theory, information theory), advanced