H&S is an Executive Search Firm.

We are looking to develop a pilot that analyses large pool of unstructured data to provide insight into the success of H&S search and as a result the success of the placement within the client’s organization.

The tool will use data from H&S systems, such as email communication, latitude data, assessment tools, notes etc and potentially augmented by a short survey of placements (1-3 years post placement date). Using this data the tool will highlight themes emerging from machine learning capabilities.

This is a intended as a pilot (proof of concept). Once the concept is proven, we may wish to expand this tool to a larger and broader set of applications and questions.


The tool will be driven by themes that emerge from the data, but there are a few broad questions to be considered:

1. How good are we at predicting a successful candidate?

2. What are the main predictors how successful a placement will be once placed?

3. Where are the opportunities for us to improve our hiring hit rate? Efficiency?


All from H&S:

• Data captured during the search process: age, years of experience, tests (WAVE etc), notes by

Search consultant

• Email communication with candidates

• Meeting requests and meetings held

Following placement (H&S data sources)

• Length of service at the job / at the same company

Consider a quick-fire survey to past placements asking 5-10 questions

• How happy are you in your role?

• What the key drivers to your success?

• Have you been promoted (considered for promotion)?


The tool will display a set of themes both in qualitative and quantitative form, identifying sources of success in placing candidates, correlation factors etc.


We would like to work with UK-based developers, as we expect close cooperation in developing the tool.

Skills: Artificial Intelligence, Coding, General Labor, Machine Learning

Location: London, United Kingdom

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Project ID: #16942258

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Hello, Further discussion is required regarding the exact need prior to accepting the project. Thank you.

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