Skyscanner is Europe's leading provider of flight, travel search and mobile apps. We are one of the most innovative and fastest growing travel companies globally and as a result we consume and generate vast quantities of data. To best take advantage of all this data presents unique challenges and opportunities (this is where you come in).
Labs is Skyscanner’s R&D team. We build prototypes to validate new ideas or demonstrate new technologies, and undertake projects where there may be a high degree of risk or uncertainty. Projects range from the highly technical, to those more focussed on user experience and are experimental in nature. However, we are still accountable for deliverables, which can take the form of business cases based on A/B tests, research papers, proof of concepts and fully implemented systems.
Data projects include recommendation engines, ranking algorithms, online learning, and price forecasting and estimation.
You will have a love of data and an established background in applying machine learning techniques to real-world problems, preferably at web-scale. You will be able to work independently, in the absence of clearly defined requirements, to pro-actively explore and deliver solutions, and then be able to clearly communicate and demonstrate their findings. You will also be able to collaborate across multi-disciplinary teams.
We’re seeking a machine learning expert, reporting to the Labs Technical Manager. The role will require a high degree of technical knowledge spanning machine learning, data modelling and analysis. The Labs team employs technologies not used elsewhere in Skyscanner so a strong Computer Science background and an enthusiasm for new technologies are key.
Skills & experience required:
Educated to degree level in a relevant subject (Computer Science, Machine Learning, Natural Language Processing, Statistics or similar), or equivalent relevant demonstrable experience (postgraduate degree) are desirable.
- Demonstrable experience delivering solutions using machine learning at scale
- Strong programming and software engineering skills in at least one modern language
- Experience with at least one statistical analysis environment
- Data wrangling, both big and small
- Self-sufficient, with a focus on delivery
- Strong written and verbal communication skills
Here are some of our favourite technologies:
- R, ggplot2, dplyr, knitr
- Python, pandas, scikit-learn, IPython Notebook
- Randomised streaming algorithms
- Hadoop, Impala
- Couchbase, Redis
- VirtualBox, Vagrant, Docker