Lead Data Scientist (Resiliency Engineering)

TL;DR

Lead Data Scientist (Resiliency Engineering): Lead the design, development, and deployment of data science solutions for Resiliency Engineering, applying statistical modeling, machine learning, and experimentation to improve system reliability and performance. Focus on translating ambiguous problems into scalable approaches, end-to-end model development including productionization and monitoring, and integrating AI/ML-enabled solutions in high-availability environments.

Location: United States - Washington - Seattle (flexible work model)

Salary: $116,500 - $163,000 (up to $186,500 based on performance)

Company

hirify.global powers global travel for everyone with cutting-edge tech and solutions for partners.

What you will do

• Lead design, development, and deployment of data science solutions for Resiliency Engineering using statistical modeling, ML, and experimentation.
• Translate ambiguous technical and business problems into scalable data science approaches and partner with engineering teams on strategy and metrics.
• Drive end-to-end model development: data exploration, feature engineering, selection, evaluation, productionization, and monitoring.
• Apply technical depth in data modeling, API integration, system design, and operationalization of analytical products.
• Integrate and operate AI/ML solutions to improve outcomes in resilient environments.
• Influence technical direction through data-driven decisions and elevate standards for scientific rigor and reusability.

Requirements

• Degree in quantitative/technical field (Bachelor’s, Master’s, or PhD) in data science, ML, statistics, CS, or related.
• Demonstrated ownership of complex data science solutions at domain/organization level, solving ambiguous problems with measurable impact.
• Strong foundation in ML, statistical analysis, experimentation, data modeling, software engineering for production, system design, and service integration.
• Experience building/deploying scalable data products/models with modern tools across technical domains.
• Experience partnering with engineering stakeholders to operationalize resilient solutions, monitor, and improve reliability via data insights.

Nice to have

• Advanced degree in data science, ML, statistics, or CS.
• Leading DS initiatives at scale in platform/infrastructure/resiliency environments, influencing architecture.
• Strength in operational excellence: model observability, lifecycle management, experimentation quality, continuous ML improvement.
• Using large-scale data/telemetry for strategic decisions, prioritizing investments, improving reliability.
• AI/ML beyond modeling: tools/workflows to accelerate insights, engineering effectiveness, resilience products.

Culture & Benefits

• Open culture guided by Values and Leadership Agreements, celebrating differences.
• Full benefits: medical/dental/vision, generous time-off, parental leave, flexible work model, career development.
• Travel perks: wellness & travel reimbursement, discounts, IATAN membership.
• Participates in E-Verify for work authorization.