Principal Applied Scientist

Independently partners with science, engineering, and product teams to translate business needs into a clear research plan and production-ready solutions. Leads end-to-end applied research: surveying state of the art, preparing high-quality datasets, running POCs/experiments, and designing, training, and optimizing ML/AI models and solution architectures. Writes reliable, well-documented code, following best practices for version control, testing, and deployment, and collaborate closely to ship, monitor, and iterate on models in production.

Distill insights for diverse audiences, contribute patents, white papers, and technical docs, and demonstrate deep expertise in a business-critical research area. Engage with the broader scientific community through publications, peer review, and internal collaborations.

• Independently collaborates with science, engineering, and product teams to identify business and product needs to create and implement solutions, promote innovation, and drive model implementations.
• Independently performs literature reviews and state-of-the-art analyses to understand the problem that needs to be solved.
• Creates a science plan to address product and/or business problems.
• Serves as a leading contributor on the identification of solutions based on the scientific process (e.g., including modeling approaches, evaluation techniques, data collections).

• Develops and pilots research proofs-of-concept (POCs) or research experiments to demonstrate the feasibility of proposed solutions, gather supporting evidence, and secure resources for applied research.
• Independently identifies and prepares datasets for use in applied research. Ensures datasets meet quality standards, and identifies gaps in the data.
• Contributes to the design of and conducts research on new theories, tools, and methodologies to transfer and apply state-of-the-art technologies into products and services or make advancements in the field.

• Designs, develops, and optimizes ML and AI technologies, such as model training/fine-tuning and computation optimizations.
• Designs and reviews the architecture of ML and AI solutions, such as data, model, training, and evaluation, employing best practices.

• Independently identifies meaningful insights from data and metadata sources from analysis and experiments.
• Interprets and communicates insights and findings to cross-level and cross-functional audience.
• Creates patents, white papers, or other technical documentation related to applied research outcomes.

• Develops efficient, bug-free, medium-complexity code from scratch, and properly maintains and organizes existing codebase.
• Implements best practices for version control, code review, and code delivery/deployment.
• Builds and maintains professional documentation for technical processes (experimentation, data collection and analyses, model building).
• Tests and reviews code for bugs.

• Collaborates with fellow technical teams to ensure the successful and timely delivery of solutions and integration of services.
• Collaborates with software engineers and/or machine learning engineers to deploy solutions into production environments.
• Participates in the peer review of solutions prior to delivery.
• Coordinates with team members for the delivery of solutions.
• Ensures delivered solutions successfully solve product and/or business problems.
• Independently collaborates with operational teams to evaluate and monitor solutions in production.

• Demonstrates advanced…