Please send your CV to firstname.lastname@example.org, with a covering letter setting out why you’re interested in the role and how you meet our criteria. You need the right to work in the UK for this role.
Who we are
Ecosy Travel is a tech for good company that’s revolutionising the way we travel. We’re developing an innovative green travel booking platform that makes it easy for everyone to find, book and share their eco escapes. We’re using novel data analytics to remove barriers to going green and support our supply chain to deliver a positive impact through their businesses.
We’re a growing team of remote workers, championing the benefits of the digital nomad lifestyle. We’re passionate about people, places and planet and are committed to utilising technology in novel ways to accelerate the transition to a regenerative tourism industry. We meet monthly and work together in-person whenever we’re in the same city.
Ecosy Travel is a certified social enterprise and pending B Corp dedicated to reducing the climate impact of travel & tourism. That means the majority of our profits will be reinvested in our social mission to cut greenhouse gas emissions in the travel & tourism sector. As a social enterprise we are also committed to creating an inclusive and supportive work culture that reflects our passion for a just transition to a green economy.
Our data scientist will lead Ecosy’s work to develop carbon-optimisation algorithms, across our software components. They will conduct development work as part of a new Innovate UK funding project to develop a novel recommendation engine that suggests next steps for holiday properties to reduce their emissions. The successful candidate will maintain and improve our carbon-optimised routing algorithm and continue work to connect third party APIs to our web application. They will also contribute to ongoing work to gamify user experience to improve user engagement and minimise emissions.
This is an exciting opportunity to join the team in the early days and, for the right candidate, grow with the company. We are offering a fixed term contract for 1 year with scope for the role to become permanent. Salary is negotiable depending on experience, up to £40,000 pa (with equity options also negotiable). We are open to discussing flexible working including job shares and flexible patterns of work. This is a remote or hybrid role. The position is available for a November start.
Who we’re looking for
● Proficiency in using Python for data science, e.g. scikit-learn, NumPy, Pandas libraries.
● Proven skills and experience developing computational algorithms to solve problems.
● Excellent and demonstrable organisational skills.
● Conscientious, and able to able to work well independently and within a team setting.
● Experience using data from external sources via API.
● Commitment to using tech innovations to unlock today’s biggest society challenges, including the climate crisis.
● Proven skills and experience conducting geospatial analysis.
● Proven skills and experience developing a route optimisation algorithm.
● Experience working with emissions data and carbon accounting methodologies.
● Interest or experience in web development.
● Ability to work effectively both independently and in a team.
● Experience with AWS or cloud computing, notably AWS Lambda, SageMaker, and Serverless Applications.
● Experience with supervised (classification), unsupervised (clustering), or recommendation models.
● Familiarity with dashboard building or data analytics.
We are a positive and supportive team and hope to provide the conditions for you to take pride in your work and deliver high quality results. We have high standards and your best work will be acknowledged and appreciated.
We are an equal opportunities employer. If you require any reasonable adjustments for accessibility during the application process, please get in touch. Demographic groups with lower representation in tech are less likely to apply if they do not meet all the requirements – we encourage you to apply even if your experience does not align completely with the skill set outlined.