Data Scientist, Applied Science

About the role

Cabify is seeking a data scientist to join our Applied Science group and influence mobility across more than 40 global cities. You will work across diverse domains including pricing, fraud prevention, and route management to transform technical research into operational improvements. In this capacity, you will own the design and execution of analytical frameworks that bridge the gap between experimental findings and large-scale implementation. The role requires you to partner closely with cross-functional stakeholders to identify opportunities where data science can unlock efficiency and enhance user experience. You will be responsible for ensuring that models are not only accurate but also robust and deployable in real-world conditions. This position demands curiosity, rigor, and the ability to communicate complex concepts to non-technical audiences. Ultimately, your work will directly contribute to measurable improvements in key business metrics and strategic decision-making.

Key facts

What you'll do

• Partner with engineering, product, and business stakeholders to define and deliver data science initiatives that support strategic objectives.
• Design, build, and validate machine learning and statistical models that are suitable for integration into production environments at scale.
• Plan and execute controlled experiments that assess the impact of new product features using scientific methods and rigorous evaluation frameworks.
• Synthesize complex analytical results into clear business insights that drive value and inform high-level decision-making processes.
• Provide data-driven perspectives during product strategy sessions to ensure that proposed solutions are grounded in empirical evidence.
• Explore and analyze large datasets using advanced statistical techniques to uncover patterns, trends, and actionable opportunities.
• Develop end-to-end modeling pipelines that emphasize reproducibility, transparency, and maintainability across the project lifecycle.
• Work autonomously to scope and solve ambiguous problems where predefined processes and structures are not fully established.
• Collaborate with domain experts to ensure that analytical approaches align with operational constraints and business priorities.
• Contribute to the continuous improvement of modeling practices by staying current with advances in machine learning and data science methodologies.
• Support the deployment of models into production settings, focusing on performance monitoring and practical implementation challenges.
• Translate technical outputs into narratives that resonate with both technical and non-technical stakeholders across the organization.
• Participate in code reviews and knowledge-sharing sessions to promote best practices and maintain high standards of analytical quality.
• Act as a technical advisor on data-related matters, helping teams integrate data thinking into their daily workflows.

Requirements

• Bring a minimum of 3 years of professional experience in data science, research, or a closely related analytical field.
• Hold a Masters degree in Computer Science, Physics, Engineering, Mathematics, Economics, or another relevant quantitative discipline.
• Demonstrate strong proficiency in probability and statistical theory with the ability to apply concepts to real-world problems.
• Show experience managing the full lifecycle of statistical and machine learning modeling from initial design to deployment.
• Possess advanced Python skills focused on data analysis, visualization, and manipulation of complex datasets.
• Exhibit the capability to write complex SQL queries for data extraction, transformation, and processing at scale.
• Operate effectively in dynamic environments where work structures are flexible and self-direction is essential.
• Communicate clearly and professionally in English, given the international nature of the teams and stakeholders.

Nice to have

• Hands-on experience deploying machine learning models to production, including setting up monitoring and maintenance strategies.
• Familiarity with deep learning, time series analysis, Bayesian statistics, or causal inference techniques.
• Background in geospatial data analysis and visualization, particularly in the context of mobility or location-based services.

Practical notes

• Salary range: 38K to 53K Euro.
• Benefits include flexible working hours, free Cabify rides, and personal development programs.
• Access to Coursera licenses and the iFeel mental health platform.
• Flexible compensation options for childcare, healthcare, transport, and restaurant expenses.
• Office amenities include a pet room, free coffee, and fruit.
• Cabify is an equal opportunity employer.