Data Scientist/ML Engineer

Responsibilities

Conduct analysis to solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Build and prototype data pipelines for analysis at scale to provide insights. Work closely with co-founders to help develop and embed cutting edge and innovative artificial intelligence and machine learning models within product. Write code in language optimal for particular task or system with bias for high-level, common languages such as Python, R, SQL, node.js, etc. Collect, clean, transform, and presents datasets to provide useful information to guide product and features development. Make careful recommendations for process improvement and business decisions. Learn new tools such as programming languages as necessary.

The ideal candidate is curious, passionate about data, programming and revolutionising meetings and has a strong technical background. The ideal candidate also has the ability to look at problems in creative ways, enjoys building solutions and sharing information and is high-energy and enjoyable to work with.

Qualifications

PHD degree or equivalent experience in an analytical human discipline such as cognition, psychology, statistics, etc.

Two years’ professional work experience in academia, industry, or equivalent time performing graduate work in technical discipline. Additional years’ experience is preferred.

The ideal candidate preferably has advanced experience working with a variety of organisational meeting dynamics and cultures, and familiarity with implementing best practices for increasing meeting productivity.

Furthermore, the ideal candidate has advanced experience with a variety of machine learning algorithms, including classification, regression, clustering, neural networks, latent semantics analysis, and natural language processing, as well as advanced programming skills in vectorised languages/packages such as R, Python, Numpy, Matlab, and experience working with JavaScript environments, such as node.js, improving work-product code from others, and completing projects from inception to delivery.

Ideally, the candidate also has experience with speech to text audio transcription APIs and technologies.