Machine learning along with artificial intelligence have become buzzwords. Given that Lokad has become identified as one of the key European companies that generate real-world decisions driven by machine learning - supply chain decisions actually - we are getting a growing number of applicants.

The good news: we are still hiring!

In this post, we review the three realms of machine learning that exist at Lokad and what you need to do to maximize the odds of getting an interview with us, and ideally be hired afterwards.

Kudos to the applicants who will be able to mention that they have read this blog post during their interview. Smart people are curious people, and if you can’t be bothered doing a bit of research on your future employer, you’re probably not fit for the machine learning industry anyway.

Job 1: Predictive business modelling

Improving the supply chain performance of a company through machine learning takes significant effort. The data need to be well-prepared. The resolution of the challenge should be fully aligned with the vision and the strategy of the client company. The supply chain teams should be coached to embrace a new and more capable analytical solution. Measurable results should be collected, and one should be be prepared to have these results challenged by top management. At Lokad, the data modelling team, or more simply put, the data team, is responsible for tackling those challenges.

For this specific position, we are looking for engineers with a strong analytical mindset who are capable not only of understanding the strengths and the limitations of the machine learning engines that are made available to them, but are also able to implement real-life set-ups that will be integrated into the daily workflows of real-world supply chains. Improvements are real and mistakes are real too. In your interview, it is advised to demonstrate your understanding of the Lokad product as documented on our website. Bonus points if you can outline how Lokad’s technology can be used to address actual supply chain challenges.

Job 2: Crafting the Big Data infrastructure

Machine learning is critically dependent on data. In fact, the more data is available, the better machine learning works. Lokad seeks talented software engineers that can design all the infrastructure that supports the different machine learning bits. The importance of the whole data pipeline is not to be underestimated: a deficient pipeline is one of the primary failure causes of data-driven initiatives. The infrastructure needs to be not only fast and reliable, but also needs to be able cope with the hefty computing requirements of the machine learning algorithms themselves.

For this role, we are looking for software engineers with a strong taste for complex distributed back-office processing. You should not be afraid of tackling complicated algorithms, such as dealing with a radix tree, and implement such algorithms yourself. Ideally, in your interview, you should demonstrate not only your capacity to understand and implement this kind of algorithmic processing, but also to deliver code that can be maintained and that is fit for production.

Job 3: Hardcore machine learning science

Most modern machine learning algorithms are complicated not only from a statistical perspective, but also from a purely algorithmic perspective. Lokad seeks talented mathematicians who are willing to acquire the software development skills it takes to implement those “hardcore” machine learning algorithms. We have developed our own suit of algorithms which are specifically designed for supply chain needs. Do not expect to plug one open source machine learning toolkit and move on: our clients are critically dependent on algorithms that have been designed to accommodate specific supply chain challenges.

For this position, we are looking for mathematicians or software developers with a strong propensity for numerical analysis and optimization, who have the ambition to deal with stunningly difficult problems. You should not be afraid of rolling out your own class of algorithms which may be somewhat unlike what is considered to be “mainstream” machine learning. Ideally, in your interview, you should be able to demonstrate why Lokad requires alternative approaches and maybe even shed some personal insights on the case.