Head of Data Science

The Head of Data Science at Carmoola will be responsible for leading the company's Credit Scoring, Fraud, and Collections analysis strategies. This includes building a cutting-edge lending platform, analyzing ways to enhance acceptance rates while managing performance, and reducing fraud risk. Moreover, the role involves proactively designing changes for our decision engine and owning the Collections strategy to improve debt recovery.

Candidates applying for the Head of Data Science role at Carmoola should possess a bachelor's or master’s degree, or a PhD in a STEM or numerical subject, with over five years of experience in a financially analytical role. A solid understanding of the regulatory environment related to responsible lending and experience in using modern data science techniques to enhance decision-making are also crucial.

For the Head of Data Science position at Carmoola, critical skills include a strong background in risk management and fraud prevention, in addition to experience preparing materials for senior audiences. Knowledge of Python programming and collections strategy experience is advantageous to succeed in this role, as is the ability to quickly learn and adapt to evolving business needs.

At Carmoola, the culture is vibrant and collaborative, encouraging open communication and teamwork. The ideal Head of Data Science will be a high-energy individual who embodies a spirit of resilience and adaptability, contributing positively to a supportive team environment as they lead the data science function from the ground up.

Working as the Head of Data Science at Carmoola offers an attractive salary package ranging from £120k to £140k, along with equity and options. Additionally, employees enjoy a hybrid working model, which includes the opportunity to work in a modern office in Primrose Hill, London, and be part of an exciting journey as the company scales within the car financing industry.

In answering this question, focus on specific projects where you developed or improved credit scoring models. Discuss the techniques you used, the data sources involved, and how your contributions improved decision-making processes or acceptance rates.

When discussing your approach to fraud risk assessment, be sure to highlight your analytical methods and any technologies you've used. Give examples of specific strategies you implemented that successfully reduced fraud incidents in previous roles.

When discussing a collections strategy, explain your process for data analysis, customer insights, and implementation. Reference any successful strategies you've developed and the results achieved, showing how you can link data science insights to practical outcomes.

In answering this, discuss specific data analysis techniques you’re proficient in, such as predictive modeling or machine learning. Also, elaborate on how you’ve applied these techniques in real-world settings to drive business results.

Share your strategies for simplifying complex data topics into understandable terms. Provide a specific example of a time when you successfully conveyed data insights to a non-technical audience, demonstrating your communication skills and ability to bridge gaps.

Provide a detailed example of a project where you employed Python to solve a specific problem or streamline processes. Highlight the libraries you used and the impact of your work on the project outcome.

Explain your familiarity with the regulatory environment affecting the fintech sector. Discuss ways you have integrated compliance into your analytical processes and how you keep up-to-date with any changes in regulations.

Here, emphasize your personal methods for learning quickly and adjusting to evolving business needs. Share anecdotes from previous experiences where adaptability was key to your success.

Discuss the importance of teamwork in your past data science projects. Provide specific examples of how you collaborated with team members to achieve shared goals and the outcomes of those collaborative efforts.

Share your passion for data science and fintech, focusing on the aspects of the job that excite you – be it solving complex problems, driving impactful decisions, or mentoring others in the field.