Lead Data Scientist

What is this role about?🚀

As the Lead Data Scientist at Bondora, you’ll be the driving force behind our data science strategy and the creation of high-impact scoring models. This is both a strategic and hands-on role — you’ll collaborate with our credit, product, engineering, and business teams to bring advanced data science to life in everyday decision-making.

You’ll lead a talented team of data scientists, ensuring model accuracy, regulatory compliance, and business alignment, while building a culture of innovation and learning 💡

Your main responsibilities 🎯

• Oversee the design, development, and implementation of scalable scoring models to drive business impact.
• Collaborate with the Credit team to define data science strategies and ensure alignment with overall business objectives.
• Guide and mentor junior data scientists, fostering a culture of innovation, continuous improvement, and best practices in model development and deployment.
• Communicate complex technical concepts to non-technical stakeholders, providing actionable insights and driving data-informed decision-making.
• Establish rigorous testing and validation protocols to ensure model accuracy, reliability, and compliance with regulatory standards.
• Stay on top of industry trends and emerging technologies, integrating them into the team’s workflow to maintain competitive advantage.
• Work closely with product, engineering, and business teams to integrate data science outputs into production systems and strategic initiatives.

What would ensure success in this role?💪

• Proven track record in developing and validating credit models, ensuring they meet both regulatory requirements and business performance standards.
• Expertise in regression, classification, ensemble methods, and time-series forecasting through both linear and nonlinear modeling.
• Strong proficiency in Python (or R) and SQL, with hands-on experience in libraries like scikit-learn, TensorFlow/PyTorch.
• Experience building and managing robust data pipelines, including ETL processes, data cleaning, and integration across disparate systems.
• Familiarity with deploying machine learning models into production environments using cloud platforms (Azure) and containerization technologies (Docker, Kubernetes).
• Proficiency in visualization tools, such as Tableau, to translate complex data insights into clear, actionable business intelligence.
• Knowledge of agile development practices to efficiently manage projects and deliver iterative improvements in dynamic environments.