We are Billie, the leading provider of Buy Now, Pay Later (BNPL) payment methods for businesses, offering B2B companies innovative digital payment services and modern checkout solutions. We are to create a new standard for business payments and have made it our mission to simplify the purchasing experience for all businesses making it a tool for growth. Our solutions are based on proprietary, machine-learning-supported risk models, fully digitized processes and a highly scalable tech platform. This makes us a deep-tech company building financial products, not the other way around. We love building simple and elegant solutions and we strive for automation and scalability.
At Billie, data is at the heart of every decision. We’re a fast-growing fintech company revolutionising B2B payments, and our Decision Science team plays a crucial role in driving our success. We’re looking for a Data Scientist with a strong business mindset—someone who thrives on ownership, enjoys solving complex challenges, and is passionate about making a meaningful impact. If you’re a data-driven problem solver eager to learn and grow, this role could be the perfect opportunity for you.

WHO WE ARE LOOKING FOR:

• Proven experience as a business problem solver, applying technical expertise to formulate, test, and validate hypotheses while summarizing and communicating findings.
• A minimum of 4 years of experience in data science, analytics, business analytics, economics, or quantitative analytics - ideally within risk, fraud, or credit analysis.
• Strong SQL skills for querying and manipulating large datasets.
• Experience with statistical methods and optimization techniques.
• Proficiency in Python (including libraries like pandas, scikit-learn, and xgboost).
• Ability to translate complex data into actionable insights and effectively communicate them across different levels of the organization.
• A strong business acumen with a passion for solving real-world business challenges.
• A proactive, self-starter mindset, with the ability to work independently and collaboratively.
• Experience with cloud platforms (e.g. AWS), data warehousing solutions (e.g. Snowflake), and version control systems (e.g. Git).
• Experience in the fintech industry is a plus.Familiarity with leveraging AI tools and technologies to enhance productivity, streamline workflows, and improve decision-making processes is a plus.

ABOUT THE ROLE:

As a Data Scientist at Billie, you’ll play a key role in tackling complex business challenges related to credit risk and fraud mitigation while directly influencing our portfolio’s performance. You’ll dive deep into data to uncover meaningful insights, shaping our strategies and driving growth. This role goes beyond building models—it’s about understanding the “why” behind the data and translating your findings into actionable recommendations that resonate across teams, from product and engineering to leadership. You’ll be an essential part of our Decision Science team, working alongside talented professionals in a collaborative, inclusive environment.

WHAT YOU’LL DO

• Apply statistical methods and machine learning to analyse complex datasets, identifying fraud and credit risk patterns while uncovering opportunities to improve existing systems.
• Design and execute experiments (including A/B testing) to validate hypotheses and measure the impact of data-driven strategies.
• Develop and implement machine learning models to predict and mitigate risk.
• Work closely with engineers, product managers, and business leaders to translate insights into concrete actions that improve our business outcomes.
• Clearly communicate findings to both technical and non-technical audiences, ensuring data-driven insights influence key business decisions.
• Continuously explore new data sources, tools, and approaches to enhance risk management strategies.
• Take ownership of projects from problem definition to solution implementation, collaborating across teams to deliver impactful solutions.
• Familiarity with leveraging AI tools and technologies to enhance productivity, streamline workflows, and improve decision-making processes is a plus.