Lead Applied Scientist

What you'll do:

As an experienced Applied Scientist, you will play a key role in shaping and delivering scalable AI/ML solutions within the POI domain. You will operate across complex challenges, driving both the technical direction and execution of high-impact initiatives across teams.
 
You will:
• Lead the design, implementation, and integration of scalable AI/ML systems and services within the POI domain, setting direction for critical technical components
• Own and deliver end-to-end AI/ML solutions for large-scale, high-impact initiatives, ensuring quality, scalability, and long-term maintainability
• Solve complex, ambiguous problems, defining approaches where no clear path exists and applying innovative, data-driven solutions
• Design and develop advanced, production-grade algorithmic solutions and ML models that address challenging real-world use cases at scale
• Shape the technical architecture within your domain, making decisions that influence systems across teams and long-term platform evolution
• Proactively identify risks, trade-offs, and technical dependencies, driving alignment and resolution across stakeholders
• Champion best practices and continuously raise the bar on model development, system design, and engineering excellence
• Produce high-quality, production-level code and lead by example through thoughtful and impactful code reviews
• Act as a go-to expert within the team, providing technical leadership and guidance in your area of expertise
• Mentor Applied Scientists (Levels I–III), supporting their growth and strengthening the overall capability of the team
• Collaborate across teams and influence Product Managers, Program Managers, and leadership on AI/ML strategy, solutions, and trade-offs

What you'll need:

• Strong, up-to-date expertise in Applied Science and Machine Learning techniques, ideally within POI, geospatial, or similarly data-intensive domains
• Proven ability to design, build, and deploy scalable AI/ML systems in production, making high-quality technical decisions that balance performance, scalability, and maintainability
• Demonstrated ability to solve complex, ambiguous problems, defining approaches where no clear precedent exists and driving solutions from concept to production
• Experience developing advanced algorithmic solutions and ML models that address real-world challenges at scale
• Strong understanding of system design and architecture, with the ability to shape technical direction and influence decisions beyond your immediate team
• Deep understanding of product context and domain-specific systems, enabling sound decision-making and effective evaluation of trade-offs
• Strong coding skills, with a focus on writing efficient, maintainable, and production-quality code in a collaborative environment
• Experience identifying and resolving technical risks, dependencies, and large-scale system challenges (e.g., refactoring, migrations, performance improvements)
• Ability to operate with a high degree of autonomy and ownership, managing multiple priorities in complex environments
• Strong collaboration and communication skills, with the ability to influence stakeholders across disciplines and explain complex concepts clearly to both technical and non-technical audiences
• Experience mentoring others and contributing to raising the bar on team capability, ways of working, and engineering excellence

Ways of working

• Operates with good level of autonomy on defined problem areas, seeking input for complex or high‑impact decisions.
• Handles problems of medium to high complexity that may involve evolving requirements, data limitations, or system dependencies.
• Makes technical decisions within agreed scope and contributes to broader design discussions.
• Works in close collaboration with other Applied Scientists, Engineers, and Product stakeholders using shared planning and review processes.

Growth, Learning & Skill Development

This role supports continuous development by enabling deeper technical expertise, broader domain understanding, and increased ownership over more complex or higher‑impact problems.
Progression occurs through demonstrated skill growth, expanded decision‑making scope, and sustained delivery of measurable outcomes, rather than tenure or background.