Responsibilities

• Conduct and coordinate research leading to large-scale ML projects for industrial AI.
• Lead applied scientists to develop experiments, algorithms, and prototypes that advance the state-of-the-art in industrial AI.
• Provide technical and scientific guidance to the team members and support their career development.
• Develop cutting-edge ML algorithms such as online regression, classification, supervised/unsupervised learning, anomaly detection, and hybrid ML algorithms.
• Lead projects with complete independence and help from PMs even when business, product, and technical strategies are not defined and the problems to be solved are not known.
• Work with PMs to define use cases, collect data, and benchmark the results.
• Work with software engineering teams to develop industrial AI platforms and applications.
• Contribute to Gauss Labs’s intellectual property pools by publishing patents and technical papers.
• Communicate effectively with senior management as well as with colleagues from science, engineering, and business backgrounds.

Key Qualifications

• Master’s degree in applied research for machine learning and data science, or a highly relevant field.
• Deep and broad expertise across several applied science disciplines, being scientifically versatile and demonstrating scientific and industrial maturity.
• 10+ years of experience in applying AI/ML to solve complex business problems for large-scale applications.
• 5+ years of experience managing machine learning and data scientists and engineers.
• Hands-on experience programming in Python, Matlab, R, Java, C++, or other programming languages.

Preferred Qualifications

• Ph.D. specializing in AI, machine learning, data science, statistics, or related fields.
• Research experience in machine learning and data science handling time-series data, predictive modeling, and anomaly detection.
• Proven achievements in developing and managing a long-term research vision and portfolio of research initiatives, with algorithms and models that have been successfully integrated into production systems.
• Project management experience working with cross-functional teams in multiple geographic locations.
• Ability to translate informal customer requirements into technical problem definitions, dealing with ambiguity and competing objectives.
• Excellent written and verbal communication skills with the ability to present complex technical information clearly and concisely to various audiences.