Lead Data Scientist

Introduction

This role encompasses designing and architecting AI/ML models, including language models, and overseeing their training, fine-tuning, and evaluation.

The ideal candidate will have experience managing the software and hardware infrastructure teams necessary for AI and ML projects and can work with multi-modal data sources (text, image, video) for vision input and multi-modal generation. Additionally, this position requires leadership skills to manage onsite and distributed teams effectively and an academic orientation to contribute to and oversee the drafting of academic papers, with a highly advantageous publishing history. Compensation will be commensurate with experience, and equity options are available. EOE.

Responsibilities

• Lead the design, architecture, and implementation of advanced AI/ML models, focusing on language processing and multi-modal data generation.
• Manage the software, hardware, and human resource systems required for AI/ML projects, ensuring optimal performance and scalability.
• Guide the team in adopting existing company or commercially available systems and developing custom solutions tailored to project needs.
• Oversee a team of data scientists and engineers, fostering a collaborative and productive environment across onsite and distributed teams. Draft and manage the production of academic papers, showcasing the team’s innovations and contributions to data science.

Requirements

• Deep expertise in AI/ML, with a track record of designing, training, and evaluating complex models, particularly in natural language processing.
• Experience with multi-modal data generation (text, image, video) and the ability to leverage this in creating innovative AI solutions.
• Strong background in managing the software and hardware infrastructure for AI/ML projects.

Qualifications

• Proven leadership skills with experience managing onsite and distributed data science teams.
• Ability to communicate complex data science concepts effectively, internally and in the academic community, with a history of publishing scholarly papers.
• Proficiency in a wide range of data science tools and platforms, with the flexibility to adopt new technologies and methodologies as required.