Manager, AI and Data Science

Premera Blue Cross•WA Western Telecommuter, WA

2d•$131,900 - $270,500•Remote

About The Position

At Premera, we're leveraging artificial intelligence and data science to solve some of healthcare's most complex challenges and create meaningful impact for the people and communities we serve. As our Manager of AI and Data Science, you'll lead a talented team of data scientists and AI innovators developing cutting-edge solutions that improve decision-making, unlock new opportunities, and shape the future of healthcare. We're looking for a visionary and technically strong leader who enjoys rolling up their sleeves, exploring new ideas, and turning emerging technologies into real-world results. You'll guide a high-performing team, influence strategy, collaborate across the organization, and help transform ambitious concepts into scalable AI-powered solutions that deliver measurable business value. If you're energized by innovation, passionate about developing people, and excited to work at the intersection of AI, data science, and healthcare, we’d love to talk to you! This is a telecommuter position, working from home.

Requirements

Bachelor’s Degree in Computer Science, Statistics, Mathematics, or a related quantitative field; or 5+ years of equivalent professional experience.
2+ years of experience leading highly analytical and scientific teams, guiding them through data science or machine learning.
5+ years of hands-on experience developing and validating AI/ML solutions in industries with a track record of effectively transitioning models from research to pilot or POC.
5+ years of experience conducting quantitative analytics.
Experience in developing and experimenting with deep learning architectures (CNNs, transformers, LSTMs, etc.) with frameworks such as TensorFlow, PyTorch, and Keras.
Proven experience at building and implementing processes, controls, and methods to support the debugging of ML/AI models and enhancing predictive performance.
Proven experience working with ML lifecycles in experimentation contexts, including hypothesis driven development practices, rapid prototyping, and iterative model refinement.
Experience applying product development lifecycle methodologies to data centric products, aligning roadmaps with market or stakeholder needs and iterating on features through user feedback and data insights.
Prior exposure to software design patterns, microservices, distributed computing, container orchestration, and other relevant architectures.
Proficient in SQL for data exploration and feature engineering, with an ability to handle structured and unstructured data in various research contexts. Comfort working with evolving data formats and tools, leveraging data management best practices to support experimentation and prototyping.
Proven experience with a variety of statistical analyses (e.g., hypothesis testing, experimental design, regression analysis, clustering, time series analysis, anomaly detection, and sequence analysis) to data-driven decision making.
In-depth experience developing supervised predictive models (e.g., logistic regression, random forests, gradient boosting) and applying unsupervised learning techniques (e.g., clustering, dimensionality reduction).
In depth knowledge of modern deep learning models, algorithms, and architectures (self/cross attention, encoders and decoders, residual connections, normalizing layers, contrastive losses and joint embeddings, multi-task learning, transfer learning, state space models).
Hands-on expertise with cutting-edge generative AI techniques, including prompt engineering, various reasoning strategies, agentic frameworks, and function-calling approaches. Experience researching and prototyping innovative retrieval-augmented generation (RAG) patterns (e.g., prompt rewriting, routing, multi-modal fusion, hierarchical navigable small world indices, reciprocal rank fusion).
Demonstrated expertise of prompt engineering techniques for generative AI—including role/system prompts, prompt rewrites, and context injection—to innovate in research and experimentation. Proven track record in designing and leading large-scale prompt engineering projects, optimizing prompt crafting for accuracy, interpretability, and creativity.
Excellent communication, collaboration, mentorship, and team leadership skills.

Nice To Haves

Advanced degree (Masters/PhD) in a quantitative, computational, or scientific field, with demonstrated expertise in AI/ML development methods.
2+ years managing cross-functional research collaborations or academic/industry partnerships with a strong focus on innovative AI/ML development.
5+ years expertise with core data science libraries (NumPy, Pandas, Matplotlib and scikit-learn) and familiarity with advanced model interpretability libraries (e.g., SHAP, LIME) and statistical tools (e.g., Statsmodels, PyMC3).
Experience with NLP libraries (e.g., Hugging Face Transformers, SpaCy) or specialized packages (e.g., XGBoost, LightGBM).
5+ years of experience productionizing and monitoring AI models and solutions with MLOps frameworks (e.g., MLflow, Kubeflow) for experiment tracking, model packaging, and versioning. Experience applying drift detection and continuous evaluation strategies to maintain the scientific and business relevance of AI/ML solutions.
5+ years working within Agile-like test and learning environments, using iterative or sprint-based approaches to develop AI/ML proofs of concept and production-ready deliverables.
5+ years of healthcare and healthcare data experience resulting in the creation of solutions and systems for the healthcare industry.
2+ years implementing responsible AI practices, including model interpretability, fairness audits, and ethical risk assessments.

Responsibilities

Build and lead a high-performing team of data scientists, machine learning scientists, and experimental engineers, fostering a culture of innovation, rigorous experimentation, and continuous learning.
Stay at the forefront and capitalize on advancements in AI by driving alignment with organizational goals.
Leads cross-functional collaborations with business, analytical, and technical teams to ideate and develop innovative AI/ML models and actionable insights addressing complex healthcare challenges.
Lead hands-on exploration, development, and iterative testing of advanced AI/ML models to drive strategic insights and innovative business solutions. Champion continuous experimentation, rapid prototyping, and data-driven validation while ensuring timely, high-quality results.
Oversee continuous model performance monitoring to detect drift and ensure ongoing validity of AI/ML solutions. Guide re-training strategies and collaborate on timely issue resolution, maintaining scientific rigor and reliability.
Collaborate with stakeholders to continuously evaluate the strategic impact and business value of AI/ML prototypes and production models, ensuring that data-driven insights translate into meaningful outcomes.
Champion and reinforce rigorous experimentation and development best practices - including peer review, reproducible experimentation, and responsible AI principles such as fairness and explainability - to maintain high standards in all AI initiatives.
Mentor and coach data scientists, machine learning scientists, and experimental engineers. Foster continuous learning, advanced skill development, and professional growth in alignment with emerging AI capabilities.
Instill a culture of iterative experimentation and flexible, agile-like development practices, emphasizing robust documentation standards for reproducibility. Encourage continuous improvement through regular retrospectives and process enhancements that accelerate development.
Establish and uphold comprehensive documentation practices for experimental design, model development, and data usage, ensuring reproducibility and compliance with all CD&A and corporate standards.
Build strong partnerships with business leaders, fostering a deep understanding of the potential and outcomes of cutting-edge AI/ML advancements. Translate complex scientific concepts into clear, compelling narratives, ensuring stakeholders can make informed, data-driven decisions.

Benefits

Medical, vision, and dental coverage with low employee premiums.
Voluntary benefit offerings, including pet insurance for paw parents.
Life and disability insurance.
Retirement programs, including a 401K employer match and, believe it or not, a pension plan that is vested after 3 years of service.
Wellness incentives with a wide range of mental well-being resources for you and your dependents, including counseling services, stress management programs, and mindfulness programs, just to name a few.
Generous paid time off to reenergize.
Tuition assistance for both undergraduate and graduate degrees.
Employee recognition program to celebrate anniversaries, team accomplishments, and more.
Commuter perks make your trip to work less impactful on the environment and your wallet.
Free convenient on-site parking.
Subsidized on-campus cafes make lunchtime connections with colleagues fun and affordable.
Participate in engaging on-site activities such as health and wellness events, coffee connects, disaster preparedness fairs and more.
Our complementary fitness & well-being center offers both in-person and virtual workouts and nutritional counseling.
Need a brain break? Challenge someone to a game of shuffleboard or ping pong while on campus.