SUMMARY

Reporting to the Senior Director, Engineering, the Principal Applied Scientist will be a senior individual contributor responsible for advancing the company’s AI/ML strategy and delivering high-impact data-driven solutions. This role will design and implement state-of-the-art machine learning models, set technical standards, and collaborate with Engineering, Product, and customer teams to ensure AI products are scalable, secure, and responsible. As one of the most senior technical experts on the team, this person will own end-to-end model development while influencing broader data science practices across the organization.

MINIMUM QUALIFICATIONS

• 10+ years of applied experience in data science, AI, or ML, with a strong record of delivering models into production.
• Expertise in one or more of: natural language processing, computer vision, multimodal AI, time-series analysis, or generative AI.
• Hands-on experience with Python and ML frameworks such as PyTorch, TensorFlow, or JAX
• Deep expertise in large language models (e.g., GPT, LLaMA, Claude, Falcon, Mistral, or custom transformer architectures).
• Proven experience in fine-tuning, prompt engineering, evaluation, and deployment of LLMs in production environments.
• Strong knowledge of cloud-based ML infrastructure (AWS, GCP, or Azure) and distributed data systems.
• Familiarity with data security, privacy, and compliance (CJIS, GDPR, SOC2, etc.).
• Proven ability to influence product and engineering teams through technical authority without direct management.
• Excellent communication and presentation skills, including the ability to explain complex AI concepts to non-technical stakeholders.
• Master’s or PhD in Computer Science, Data Science, Statistics, or a related field strongly preferred.
• Ability to travel as required.

• Act as a senior IC and subject matter expert for AI/ML/LLM initiatives, setting technical direction in alignment with the Senior Director, Engineering.
• Research, prototype, and deliver advanced machine learning and deep learning models for real-world production applications.
• Own the full lifecycle of AI models: data acquisition, feature engineering, model design, evaluation, deployment, monitoring, and continuous improvement.
• Establish and enforce best practices for MLOps, model governance, and responsible AI, ensuring compliance with enterprise and regulatory standards.
• Partner with engineers to integrate models into production systems with an emphasis on performance, reliability, and explainability.
• Collaborate with Product teams to translate business requirements into AI/ML technical roadmaps and deliverables.
• Produce high-quality technical documentation, design artifacts, and knowledge transfer materials.
• Evaluate and introduce new methods, frameworks, and technologies that keep the company at the forefront of AI innovation.
• Represent the Data Science team in customer and executive discussions, articulating technical details clearly and persuasively.