Principal Applied Scientist

• Educational Background: Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Electrical Engineering, or other related technical field (e.g., mathematics, physics, statistics, or similar). 

• Experience: At least 8+ years of industry experience, with strong proficiency in coding in Python for software development and data science. (Experience with additional programming languages is a plus but not required.) 

• Skills: Proficiency in Python-based machine learning frameworks and libraries such as PyTorch and TensorFlow, and experience developing and deploying generative AI or machine learning models into production. Strong communication and presentation skills, with the ability to convey complex technical concepts to diverse audiences. 

• Problem-Solving: Demonstrated ability and motivation to understand complex business contexts and to solve hard, open-ended problems. A proactive mindset in tackling challenges and optimizing solutions to deliver business impact. 

Additional or Preferred Qualifications 

• Advanced Degree (Preferred): Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field and 9+ years of related experience; OR Doctorate in one of the above fields and 6+ years related experience; OR equivalent industry experience. 

• Research & Publications: 5+ years of experience contributing to research publications (e.g., patents, academic papers, open-source libraries) that showcase innovative thinking and technical depth. 

• Research Excellence: 5+ years of experience conducting original research as part of a formal research program (in academic or industrial settings), with proven ability to drive insights from concept to execution. 

• Production Deployment: 3+ years of experience developing and deploying live production systems as part of a product team, ensuring solutions are scalable, reliable, and maintainable. 

• Product Lifecycle Experience: 3+ years of experience contributing to products or systems at multiple stages of the development lifecycle, from initial ideation and prototyping to final production deployment and ongoing iteration.