Who we are:
Bellingcat is an independent international collective of researchers, investigators and citizen journalists using open source and social media investigation to probe a variety of subjects: crime, conflicts, corruption, secret operations, mis- and disinformation, extremist groups and many others. We have received multiple awards for our groundbreaking research and publications that shine a light on crimes and help bring perpetrators to account. Our people, which include paid staff, contributors and volunteers in more than 20 countries, work in a unique field where advanced technology, forensic research, journalism, investigations, transparency and accountability come together. Do you want to join this exciting adventure?
Who we are looking for:
We are looking for a data scientist with web development skills who will help us discover openly accessible online information to turn it into a wide range of digital investigations. The primary focus will be to develop open source investigation tools, based on needs identified by our staff researchers and the global community of our contributors and volunteers. Additionally, the candidate should have experience in analyzing, researching and building machine-learning tools that will simplify the process of complex investigations.
Who you are:
The ideal candidate is enthusiastic about working for an international nonprofit organization that uses publicly available data and citizen journalist analysis to investigate various topics of public interest that advance transparency and brings perpetrators to account. Proven track record of working in the field of digital investigations is a must.
- Support Bellingcat researchers during a wide range of open source investigations
- Develop open source investigation tools for Bellingcat based on researchers’ needs and help build a new tools section on the organization’s website
- Collaborate with tech partners on projects related to open source investigations
- Enhancing data collection procedures to include information that is relevant for building analytic systems
Qualifications & Skills:
- Excellent knowledge (experience) with several programming languages. Python or R are required, plus additional languages
- Proven track record of data mining, social network/data set analysis, web scraping and/or automating open source investigations through the use of data models and custom scripts
- Background in building web based prototypes, designing experiments and evaluating results with cloud platform services like AWS
- Experience with using machine learning models and frameworks (e.g, tensorflow and other ML libraries)
- Strong interest in and experience with open source investigations
- Strong mathematical background
- Ability to process and verify integrity of data used for analysis
- Experimental and inquiring mindset, a team-oriented working style and the ability to closely work with non-technical colleagues but at the same time to independently develop technical solutions for research challenges
- English proficiency
- The candidate must be able and ready to relocate to the Netherlands. For one or two days per week, home-office might be negotiated (except during full-week workshops/events)
- Willingness to support Bellingcat international workshops as a trainer (and willingness to travel)
- Cyber security awareness.
Bellingcat is committed to a diverse working environment. All qualified candidates will receive consideration without regard to race, color, origin, religion, age, gender or sexual orientation. Your formal education is less important to us than your previous work experience in the field.
We offer a competitive salary for this position (within the range of the nonprofit sector).
Please apply until 19 February 2020 by answering the following questionnaire directly in an email to: firstname.lastname@example.org. Short-listed candidates will be invited for the next selection round in February or the first half of March. Due to the expected high volume of applications it is unfortunately not possible for us to provide individual feedback.