Solving business challenges with machine learning Read More
By utilizing psychometric and forensic data analysis, Flexy matches the best candidates to the most suited jobs, maximising job satisfaction for workers, and in turn maximising productivity for the employer.
Flexy takes into account a number of attributes to determine candidate suitability for specific job including personality traits, experience, reliability and performance related metrics. These attributes contribute to candidate’s overall suitability score for the job. Nevertheless, employers may want to prioritise differently which attributes are more important for their workforce and job type. Instead of hardcoding the weighting on candidates’ attributes, Flexy’s search engine is capable of continuously learning weights on attributes from data employers generate in the system.
Digitalist Group is helping Flexy to customise matching algorithms per employer. On top of that we also train, deploy and evaluate a number of machine learning models (e.g. recommender systems, clustering and regression models) aimed at solving key business challenges including initiatives for reducing “No Shows” (individuals not turning up for their shifts) and real-time hourly rate suggestions based on job category, time and location.
In addition to this Digitalist design team was tasked with delivering the UI concepts and visual language for all key customer touch points of Flexy service.
When we set up Flexy we had a clear vision in mind, to create clarity and improve efficiency in the temporary employment market. In order to achieve this objective, we've had to embrace new technologies and explore ways in which we could replace analogue processes with AI-led digital solutions. Working with Digitalist Group we were provided with the guidance and thought leadership that we needed to not only re-define the matching of candidates and jobs but to also translate the complex and sophisticated machine learning algorithms to be presented in a clear and intuitive user interface.