Responsibilities

• Develop cutting-edge ML algorithms esp. focusing on the time-series domain but also in areas such as online classification, regression, supervised/unsupervised learning, reinforcement learning, anomaly detection, pattern recognition, and hybrid ML algorithms.
• Lead projects with complete independence and help program/product managers even when business, product, and technical strategies are evolving and the problems to be solved are not well understood.
• Work with program/project managers to define use cases, collect data, and benchmark the results.
• Collaborate with other applied scientists to experiment, and develop algorithms/ prototypes that advance the state-of-the-art in industrial AI.
• Work with software engineers to provide scaling and production support for ML/AI algorithms
• Contribute to Gauss Labs’s intellectual property pools through patents and technical publications.
• Contribute to Gauss Labs’s research advancement by publishing technical papers at external conferences and journals.

Key Qualifications

• Ph.D. degree in Machine Learning, Computer Science, Statistics, Electrical Engineering, or related fields.
• At least 10 years of experience doing exceptional ML/AI research as demonstrated by both scientific publications in top venues and potential industrial impact
• Has deep and broad expertise across several applied science disciplines, is scientifically versatile, and demonstrates scientific and industrial maturity.
• Practical 5+ years experience in applying ML/AI to complex business problems.
• 5 years of experience managing teams of research scientists/engineers and data scientists (experience managing geographically distributed, multi-cultural teams a plus)
• Exceptional project management skills
• Hands-on experience programming in Python, Matlab, R, Java, C++, or other programming languages.
• Experience in large-scale ML systems and enabling technologies including commercial cloud stacks, resource provisioning/orchestration, and scaling methodologies (e.g., distributed optimization)