From a pile of CVs to a ranked shortlist of R&D talent.

The story

Problem, product, payoff.

1. 01

   The challenge

   R&D hiring is slow and specialist. Recruiters were reading hundreds of unstructured CVs to find a few people with the right research skills, and the best candidates were often hired elsewhere before a shortlist was ready. Candidates, in turn, had to apply role by role.

2. 02

   What we built

   We put machine learning at the centre. CVs and job descriptions are parsed into structured, skill-tagged profiles; a matching engine ranks candidates for every role; and the platform sources talent from sign-ups and from academic and professional sources. Video interviews, background screening, live chat and an admin panel for plans, payments and users complete the hiring loop in one product.

3. 03

   The outcome

   Hiring teams now start from a ranked shortlist instead of a stack of PDFs, and candidates can sit back while employers invite them to interview. RND Match reports around 50 sourcing hours saved per hire, with 85% of shortlisted candidates accepted for interview by hiring teams.

Inside the product

The screens RND Match runs on.

1. 01 / 03

   CV parsing

   Candidates upload a CV and their profile fills itself in. Unstructured CVs become structured, enriched data that can be matched against jobs.

   Profile · Update your CV

   

2. 02 / 03

   Embedded skills API

   Skills are extracted automatically from CVs and recommendations, so hiring managers see a candidate's real skill set up front and can filter by experience, location and preferences.

   Search · Experience and job preferences

   

3. 03 / 03

   Job parsing and matching

   Job descriptions are converted into structured requirements, then matched against every profile to surface the best-fit people for each position.

   Positions · Matched candidates

   

Profile · Update your CV

Search · Experience and job preferences

Positions · Matched candidates