Mission statement

Companies are increasingly making data driven decisions. Our goal at Logical Glue is to put machine learning directly into the hands of business domain experts and analysts, not just data scientists, so they can rapidly extract and operationalize highly accurate and actionable insights from their data.

About the team

Logical Glue is a small yet diverse team with experience ranging from academic and industrial research to building and delivering mission critical, cutting-edge software. Changing the market’s approach to data science is an ambitious goal and requires our team to be agile, passionate and innovative.

If you are interested in working on next generation computational intelligence techniques, solving problems that incorporates cloud infrastructure, distributed systems and cutting edge web applications then we’re the team for you!

What we do

We are breaking new ground in developing a highly intuitive, self service prescriptive analytics platform complete with elegant web services, data visualisations and analytics, underpinned by a highly scalable distributed architecture with our own unique, highly predictive machine learning algorithms.

Making data science accessible for business users is no easy task. But that’s what makes working at Logical Glue so challenging and rewarding.

About you

You’re a self starter who has the drive, integrity and passion to succeed in a fast-growing startup. You're a team player and can adapt to meet the needs of our rapidly evolving business environment

You are passionate about technology, you follow industry trends, you attend meetups and experiment with new technologies and frameworks.

You are well versed in computer science fundamentals, have experience in building and delivering production systems and understand the value of testing, continuous integration, code reviews.

Core skills

  • Good understanding of Java 8 or similar.
  • Good troubleshooting skills and willingness to help in customer support duties as needed.
  • Experience with relational databases, preferably PostgreSQL.

Nice to haves

  • Prior devops/systems administration experience in a Linux environment (Ubuntu).
  • Working knowledge of web and network protocols and standards (HTTP, TLS, WebSockets, DNS, TCP etc).
  • Experience with Amazon Web Services.
  • Experience with configuration management tools and a natural tendency to automate.
  • Experience in networking, security, hardware and OS performance tuning.
  • Understanding of big data technology stack.
  • Experience with Javascript frameworks (AngularJS, React, D3).
  • Interest in statistics and machine learning.


A Data Scientist at Logical Glue needs to have statistical, mathematical, predictive modelling as well as business strategy skills to build the algorithms necessary to ask the right questions and find the right answers. You also need to be able to communicate your findings, orally and visually. You need to understand how the products are developed and even more important, as big data touches the privacy of consumers, they need to have a set of ethical responsibilities.

Apart from the skills that big data scientists can learn in university, you also need to have a special set of personality traits. You need to be very curious person, who enjoys diving deep into the material to find an answer to a yet unknown question. You need to have a natural desire to go beneath the surface of a problem. You need to be a thinker who can ask the right (business) questions. You need to be confident and self-secure as they more often than not will have to deal with situations where there is a lot unknown. You need to be patient as finding the unknown in massive data sets will take a lot of time and developing the algorithm to uncover new insights will often go by trial-and-error. You need to be able to see examples in totally different industries and be able to plot that on their current problem.

A big data scientist understands how to integrate multiple systems and data sets. They need to be able to link and mash up distinctive data sets to discover new insights. This often requires connecting different types of data sets in different forms as well as being able to work with potentially incomplete data sources and cleaning data sets to be able to use them. Of course the big data scientist needs to be able to program, preferably in different programming languages such as Python, R, Java, Ruby, Clojure, Matlab, Pig or SQL. They need to have an understanding of Hadoop, Hive and/or MapReduce. In addition the need to be familiar with disciplines such as:

  • Natural Language Processing: the interactions between computers and humans;
  • Machine learning: using computers to improve as well as develop algorithms;
  • Conceptual modelling: to be able to share and articulate modelling;
  • Statistical analysis: to understand and work around possible limitations in models;
  • Predictive modelling: most of the big data problems are towards being able to predict future outcomes;
  • Hypothesis testing: being able to develop hypothesis and test them with careful experiments.

The exact background of a big data scientist is of less importance. Fantastic data scientists can have different backgrounds such as econometrics, physics, biostatistics, computer science, applied mathematics or engineering. Most of the time the background is a Master's Degree or even PhD. However, to be successful data scientists should have at some idea of how and why data and predictive analytics are important to business.