An innovative talent acquisition and assessment strategy for IMC Trading

Candidate assessment is a key component in every talent strategy. They are designed to provide a way in which to measure performance and qualify candidates to perform a role. 

But as technical roles evolve with the adoption of new data science techniques and machine learning methodologies, organisations must also change their talent attraction and technical assessment processes to match.

Evaluating and giving feedback on complex technical testing exercises requires technical expertise. In turn, this increases the pressure on your technical specialists as manually reviewing code submissions to ensure correctness, to assess code performance and to provide the suitable technical infrastructure for students is extremely challenging and a huge time-waster.

 

For talent acquisition and assessment to become more efficient and effective, companies can innovate and change their approach in five core ways:

  • Ensure candidates can easily run tests and see the results of their code implementation
  • Provide candidates with instant feedback to create the best possible student experience
  • Remove technical challenges such as version and installation problems with Python
  • Monitor activity and capture information about each candidate's technical skills
  • Identify top talents, in real-time, based on their coding skills for targeted relationship management

Case Study

These five requirements were used to streamline the talent attraction strategy at IMC Trading - a world-leading trading firm. IMC Trading use Cambridge Spark’s proprietary training and assessment platform K.A.T.E.® to automate the evaluation of technical candidates.

Problem

IMC Trading was looking for a solution to efficiently assess and evaluate technical candidates as part of an innovative programming competition. The competition required potential candidates to implement quantitative finance strategies. The objective was to be able to evaluate participants based on coding ability, mathematical aptitude and creativity. Unfortunately, providing 1-to-1 support and feedback to each student during the implementation process would be resource intensive and exceed the competition time frame.

Solution

K.A.T.E.® provided the solution to this problem by making the whole process of submitting and testing solutions smooth and robust. As a result, K.A.T.E.® enabled the students to iterate frequently in their development and also provide IMC with full details of the student activity and engagement.

Using K.A.T.E.® IMC launched a Quantitative Finance challenge allowing candidates to experience training their own option pricing models on real-world data. IMC used K.A.T.E.® to inject custom challenges and evaluation metrics relevant to the trading undertaken at IMC, to assess each candidate's coding skills according to the specific job requirements. Then K.A.T.E.® was utilised during IMC’s programming competition to manage the influx of candidate code submissions.

Results

Every student team used K.A.T.E.® to develop their pricing model. I think K.A.T.E.® made the whole process of submitting and testing solutions very smooth. The K.A.T.E.® dashboard was very useful for us. We were able to keep an eye on the general progress of all the teams. Using the dashboard, we could also look into the code submissions directly to spot talented students.

Heiko Schäfer, Equity Options Quantitative Analyst, IMC.

“We have been running workshops for students for 3 years now. The culmination is a coding competition to implement a simple numerical option pricing model in Python. This year we used K.A.T.E.® for the first time and the result was very positive: we saw more teams finish with a working model than in the years before," said Heiko Schäfer. "Previously, we had struggled to help all the teams but now they could figure a lot of things out themselves, immediately. The vast majority of groups had a working implementation in the end and this was to a large part due to K.A.T.E.®.”

“The instant feedback from K.A.T.E.® enabled the students to iterate more frequently in their development. It was also noticeable that K.A.T.E.® helped the less computer savvy of the students a lot,” said Heiko Schäfer. “Technical problems with Python versions and installations did not impede the competition as development with K.A.T.E.® does not depend on locally running Python."

Overall K.A.T.E.® helped significantly to make our coding workshop a big success.

Heiko Schäfer, Equity Options Quantitative Analyst, IMC.

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