If you need an IT partner to collaborate with in a data science project, you’ve come to the right place. We are not just passionate about Clojure, we also want to make a real difference. We’ve therefore ensured that our team can support and conduct data science projects and research, whether it is with our knowledge or with sponsorship. If you’re planning a scientific research project or have an ongoing project that needs support, please reach out to us and we will get back to you with a suggested way forward.

At Flexiana, we have multiple developers with experience from data science projects and research. We also use Metabase to bring data to life in beautiful visualizations. Externally, these are some data science researchers and developers that we are currently sponsoring:


Why Clojure?

Clojure is ideal for conducting a scientific software project. It is a powerful programming language that combines the interactivity of a scripting language with the speed of a compiled language. It is an appropriate, practical, and flexible language to fulfill a data scientist’s different needs, thanks to its large ecosystem of native libraries and an incredibly straightforward and consistent functional approach to data manipulation, which maps closely to a mathematical formula.



If you’re looking for more learning resources, a data science stack, dev groups, or open-source mentoring, we’d recommend you check out SciCloj. Scicloj is an open, free, and dynamic hub for building a Clojure ecosystem for data science, scientific computing, and data engineering.

We have a good collaboration with several key members of the SciCloj community. Additionally, two SciCloj members (one current, and one former) happen to also be working for Flexiana.


Data Science Cases

Our team members have previous experience working with data science, here are some examples of projects they have participated in.

  1. Designing chatbots for an online commerce companyDifferential privacy
  2. Designing Data PipelinesData Pipelines
  3. ML libraries for functional and mainstream languagesMachine-learning
  4. Adding plugins to Solr engineNatural Language Processing & Data mining
  5. Compiled sensor regressionNeural networks
  6. Differential privacy over pangenome haplotypesDifferential privacy