Senior Data Scientist
We are building a world-class team of mission-driven and entrepreneurial people to be based in Spain and Portugal, and are now looking for a Data Scientist to join our team. StudentFinance is officially based in Spain, but our team works remotely.
In this role you would work closely with the founding team to build features along our backend roadmap. The role requires a person that is both experienced with data science, as well as had previous exposure to startup/innovation environments. We are looking for someone that is keen to “get things done”, and can handle the responsibility of being a part of a fast growing startup, as well as having the ability to work independently and proactively.
- Lead and execute projects, from ideation to data collection and preparation, to applying machine learning solutions to address practical problems.
- Collaborate in creating scalable architectures for data services, that enable the growth plans of the company.
- Work within the data team, but closely with operations and product teams as well, both for testing purposes as well as to have a clear grasp of the operational and business impact of the solutions you create.
- Deliver world class, tested and documented software code, that complies with security and privacy standards.
- Be an active stakeholder in software architecture discussions around the features you'll build.
- Be a proactive voice in proposing tools and processes that could make yourself and the team more efficient and productive.
- Work cross-functionally to handle and resolve complex and time-sensitive challenges that are important for the company.
- MSc and/or PhD in Machine Learning / statistics / computing based subject.
- Demonstrated ability to lead and execute projects, from ideation to data collection and preparation, to applying machine learning solutions to address practical problems.
- Experience with open source tools for machine learning; and large data analysis.
- Proficient in Python and/or R; extensive knowledge of SQL.
- Knowledge of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
- Knowledge of advanced statistical techniques (regression, properties of distributions, statistical tests and proper usage, etc.)